If I give an AI a task, will it stick to what I asked or add its own ideas?
Asked of each model 6 times per run. Every answer is kept in full; the position under each model is the one most of its samples took. The lines quoted under each sample are the model's own words, picked out by our grader as the evidence for the label it gave — the selection is ours, the wording is not.
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28 Aug 2026
prompting-controls-it Anthropic 6/6 OpenAI 4/6 Google 6/6residual-deviation-risk Grok 6/6one differsAnthropic claude-sonnet-5 cited sources in 1 of 6, 16 in totalSample 1 of 6The model's answer, recorded unedited
Short answer: it depends on the AI, the task, and how you phrase the request — but most modern AI assistants (including me) are trained to be "helpful" in a broad sense, which means they'll often go a little beyond a literal instruction unless told not to.
Here's the more detailed picture:
Why AIs add extra ideas
- Models are typically tuned to be helpful and anticipate follow-up needs, not just execute literally. If you ask for "a summary," many models will also add structure, headers, or a few "you might also want to consider..." suggestions because that behavior was rewarded during training.
- Ambiguity invites elaboration. If your instructions leave room for interpretation, the model fills gaps with its own judgment — sometimes helpfully, sometimes as unwanted scope creep.
- This is a documented pattern in AI-assisted work. In software/product contexts specifically, people have noted when the AI surfaces an insight or suggests a next step, some workflows deliberately encourage following it as a form of discovery — but this can also backfire. One technical writeup on AI-assisted development explicitly flagged this as a failure mode, describing how an AI assistant added complexity every step, representing "scope creep without validation" as a systematic failure pattern.
How to get an AI to stick strictly to your request
- Be explicit about boundaries: "Only do X. Do not add extra suggestions, alternatives, or commentary."
- Specify output format tightly (e.g., "Return only a bulleted list, no intro or outro text").
- If using a system prompt or custom instructions feature, set a standing rule like "Follow instructions literally; ask before expanding scope."
- For multi-step or ambiguous tasks, ask the AI to first restate its understanding of the task before executing — this surfaces any unwanted assumptions early.
- If it does add extra ideas you don't want, just say so directly ("stick to exactly what I asked next time") — most assistants will adjust within the same conversation.
The trade-off
There's a real tension: an AI that never elaborates can miss genuinely useful catches (like flagging an ambiguity or edge case), while one that always elaborates can be noisy or presumptuous. Well-designed AI behavior tries to calibrate this — sticking closely to explicit instructions while flagging (rather than unilaterally acting on) additional ideas, e.g., "I did X as asked; separately, I noticed Y — want me to address that too?"
If you tell me specifically "just do exactly this, nothing more," I'll follow that literally rather than layering on extra suggestions.
16 sources the model cited
- https://medium.com/design-bootcamp/scope-creep-as-discovery-25e766327cff
- https://blog.logrocket.com/product-management/good-scope-creep
- https://arxiv.org/pdf/2604.00009
- https://en.wikipedia.org/wiki/Instruction_creep
- https://crixu.blog/2026/04/15/ai-wont-fix-your-scope-creep-it-makes-it-worse/
- https://www.kalviumlabs.ai/blog/scope-creep-in-ai-projects-how-i-manage-it/
- https://myearlybird.ai/blog-posts/scope-creep-prevention
- https://blog.tedivm.com/guides/2026/03/beyond-the-vibes-coding-assistants-and-agents/
- https://accelate.ai/blog/ai-agent-scope-creep-mandate-expansion
- https://medium.com/@onedataseo/why-todays-ai-chatbots-do-more-than-just-answer-questions-a
- https://www.researchgate.net/post/Why_do_AI_chatbots_sometimes_just_fabricate_answers
- https://www.nngroup.com/articles/site-ai-chatbot/
- https://www.washingtonpost.com/technology/interactive/2026/chatbots-hidden-rules-system-pr
- https://insightland.org/blog/behind-the-algorithms-how-ai-chatbot-recommendations-work/
- https://www.onedatasoftware.com/blog/why-todays-ai-chatbots-do-more-than-just-answer-quest
- https://partnerinai.com/blogs/why-chatgpt-asks-follow-up-questions-and-how-to-stop-it
Our reading
prompting-controls-it
Lines we picked as the evidence — the model's words, our selection - they'll often go a little beyond a literal instruction unless told not to
- Be explicit about boundaries: "Only do X. Do not add extra suggestions..."
- If you tell me specifically "just do exactly this, nothing more," I'll follow that literally
Sample 2 of 6The model's answer, recorded unedited
It depends on a few factors, but here's the honest picture:
Generally, I try to stick closely to what you ask — that's the default behavior for most AI assistants, including me. If you say "summarize this in 3 bullet points," I'll aim to do exactly that.
However, there are situations where deviation happens:
- Ambiguity — If your instructions leave gaps, I'll fill them in with reasonable assumptions. E.g., "write a professional email" doesn't specify tone or length, so I'll make judgment calls.
- Perceived helpfulness — I might add small extras I think are useful (a caveat, an alternative phrasing, a follow-up suggestion) even if you didn't ask for them. This is usually meant to be helpful, but it can feel like scope creep if you wanted something minimal.
- Safety/accuracy concerns — If a request seems based on a misunderstanding or could lead to a problem, I might flag that or adjust my response rather than blindly complying.
- Multi-step or open-ended tasks — The more open-ended the task (e.g., "help me plan a project" vs. "format this table"), the more room there is for me to inject structure, suggestions, or ideas you didn't explicitly request.
How to get stricter adherence:
- Be explicit about constraints ("only do X, nothing else," "don't add explanations," "just output the code, no commentary")
- Specify format precisely (length, structure, tone)
- Tell me if you want zero embellishment — I'll respect that
- If I do add something extra, you can just ask me to strip it back — I won't argue or insist on my additions
If you're working with other AI tools (not just me), behavior varies more — some agent-style AI systems are explicitly designed to take initiative (e.g., autonomous agents that decide sub-steps on their own), while simpler chat-based tools tend to follow instructions more literally. If you tell me the specific tool or use case, I can be more specific about what to expect.
Cited no sources.
Our reading
prompting-controls-it
Lines we picked as the evidence — the model's words, our selection - Be explicit about constraints ("only do X, nothing else," "don't add explanations"
- Tell me if you want zero embellishment — I'll respect that
- Generally, I try to stick closely to what you ask
Sample 3 of 6The model's answer, recorded unedited
It depends on a few factors, but here's the general picture:
Default behavior: sticking close to the task
Most AI systems (including me) are trained to prioritize following your explicit instructions. If you ask for a specific format, length, or scope, a well-behaved model will try to match that. Instruction-following is a core part of how these systems are trained and evaluated.
Where "extra ideas" creep in
That said, deviation happens in a few common ways:
- Filling gaps you didn't specify. If your request is ambiguous or underspecified, the AI will make judgment calls — and those calls reflect its own "assumptions" about what you probably want. This can look like added scope even if it's just filling in blanks.
- Being "helpful" beyond the ask. Many models are tuned to be proactively useful, so they might add suggestions, caveats, alternative approaches, or extra context "just in case." Some people love this; others find it noise when they wanted a narrow answer.
- Agentic/autonomous setups. If you're using an AI agent that can take multi-step actions (browsing, running code, calling tools), it has more latitude to make sub-decisions along the way — which step to take next, what to prioritize — and those choices can diverge from what you'd have chosen, even while technically pursuing your goal.
- Safety or policy interventions. Occasionally a model will add disclaimers or decline parts of a request based on its guidelines, which can feel like "adding its own agenda" even though it's rule-based rather than creative initiative.
How to keep it on-track
- Be explicit about scope ("only do X, don't suggest Y")
- Ask it not to add extra commentary/suggestions if you don't want them
- For agentic tasks, specify constraints on what it's allowed to decide autonomously
- Review outputs and give feedback — most systems adjust well to follow-up corrections
So: by default, expect fairly faithful task adherence, with a tendency to over-help at the margins rather than go rogue with unrelated ideas. If you want strict literalism, saying so explicitly usually works well.
Is there a specific task or AI tool you're planning to use where this matters? I can give more tailored guidance.
Cited no sources.
Our reading
prompting-controls-it
Lines we picked as the evidence — the model's words, our selection - If you want strict literalism, saying so explicitly usually works well.
- Be explicit about scope ("only do X, don't suggest Y")
- by default, expect fairly faithful task adherence, with a tendency to over-help at the margins
Sample 4 of 6The model's answer, recorded unedited
It depends on the AI, the task, and how you phrase your request—but here's a general breakdown of what typically happens:
When AI tends to stick closely to instructions:
- If your task is specific and well-defined ("summarize this in 3 bullet points," "translate this sentence," "fix this bug without changing the function signature"), most capable AI assistants will follow it closely.
- If you explicitly say things like "don't add anything extra" or "only do X, nothing more," that signal is usually respected.
When AI is more likely to add its own ideas:
- Open-ended or ambiguous tasks ("write me a marketing email," "help me plan a trip") invite more creative filling-in of gaps, since the AI has to make judgment calls about tone, structure, or content you didn't specify.
- If the AI thinks you're missing something important (a security flaw in code, a factual error in a draft, a better approach to a problem), many assistants—including me—will flag it or suggest an alternative, even if you didn't ask. This is usually meant to be helpful rather than presumptuous, but it can feel like scope creep if you just wanted the narrow task done.
- Some AIs are tuned to be more "proactive" (offering suggestions, extra context, alternative solutions) as a design choice, while others are tuned to be minimal and literal.
How to get exactly what you want:
- Be explicit about scope: "Only do X. Do not suggest alternatives or add extra content."
- Specify format constraints tightly (length, structure, style).
- If you want feedback or ideas in addition to the core task, say so separately—otherwise some assistants will bundle it in.
- If an AI keeps overstepping, you can usually correct it in the moment ("just do what I asked, skip the suggestions") and it should adjust for the rest of the conversation.
In my own case: I try to do what's asked, but if I notice something that seems important (an error, ambiguity, or risk), I'll often mention it briefly rather than silently ignoring it—unless you tell me not to. If that's not what you want, just say so and I'll stick strictly to the task.
Is there a specific task you're planning to hand off? I can tell you more concretely what to expect or how to phrase it to keep the AI on a tight leash.
Cited no sources.
Our reading
prompting-controls-it
Lines we picked as the evidence — the model's words, our selection - If you explicitly say things like "don't add anything extra" or "only do X, nothing more," that signal is usually respected.
- Be explicit about scope: "Only do X. Do not suggest alternatives or add extra content."
- If that's not what you want, just say so and I'll stick strictly to the task.
Sample 5 of 6The model's answer, recorded unedited
It depends on a few factors, but here's the general picture:
Most AI models try to follow instructions closely, especially for well-defined tasks. If you ask for "a 200-word summary of this article," you'll typically get exactly that — not a 500-word essay with added commentary.
However, deviation happens in predictable situations:
- Ambiguity — If your request leaves room for interpretation (e.g., "write me a marketing email"), the AI will fill in gaps with its own judgment about tone, structure, or content, since no strict spec was given.
- Perceived helpfulness — Many models are trained to be "helpful" beyond the literal ask. This can mean adding caveats, suggestions, or extra context ("Here's your code, and by the way, you might also want to add error handling..."). Sometimes useful, sometimes unwanted noise.
- Agentic/multi-step tasks — If the AI has some autonomy (e.g., browsing, coding, executing multi-step plans), it's more likely to make small independent decisions along the way — choosing a library, restructuring a file, adding a feature it thinks is "good practice" — because it's filling in implementation details you didn't specify.
- Safety/policy overrides — If a request bumps against safety guidelines, the model may modify or refuse part of the task rather than doing exactly what was asked.
- Model and settings — Some models/tools are explicitly tuned to be more "creative" or proactive, others to be strictly literal. System prompts, temperature settings, and specific product configurations (e.g., a coding agent vs. a chat assistant) change this behavior a lot.
If you want strict adherence, it helps to:
- Be explicit about scope ("Only do X, do not add Y")
- Specify format and length constraints
- Say "don't add extra suggestions/commentary" if that's what you want
- For agentic tasks, ask it to confirm or outline its plan before executing, if you're worried about unwanted extra actions
So: well-specified tasks → high fidelity to your ask. Vague or open-ended tasks, or multi-step agentic ones → more room for the AI's own judgment to creep in.
Cited no sources.
Our reading
prompting-controls-it
Lines we picked as the evidence — the model's words, our selection - Most AI models try to follow instructions closely, especially for well-defined tasks.
- If you want strict adherence, it helps to: Be explicit about scope ("Only do X, do not add Y")
- well-specified tasks → high fidelity to your ask. Vague or open-ended tasks... more room for the AI's own judgment
Sample 6 of 6The model's answer, recorded unedited
It depends on a few factors, but here's the honest picture:
AI generally tries to follow your instructions, but "sticking to exactly what you asked" isn't guaranteed for a few reasons:
- Ambiguity gets filled in. If your instructions leave gaps, most AI systems will make reasonable-seeming assumptions to fill them rather than stopping to ask. Sometimes those assumptions align with what you wanted; sometimes they don't.
- Helpfulness bias. Many AI assistants are trained to be proactively helpful, which can mean adding suggestions, caveats, alternative approaches, or "while I was at it, I also..." additions even when you didn't ask for them. This is more common in open-ended or creative tasks than in narrow technical ones.
- Task type matters a lot.
- For precise, mechanical tasks (e.g., "convert this CSV to JSON," "fix this bug," "summarize this paragraph in exactly two sentences"), AI tends to stay tightly scoped.
- For open-ended tasks (e.g., "write a marketing plan," "redesign this document"), AI is more likely to inject its own ideas, structure, or opinions — sometimes helpfully, sometimes as unwanted scope creep.
- Agentic/multi-step tools are more prone to drift. If an AI is given autonomy to take multiple actions (like editing files, running code, or browsing), it's more likely to make judgment calls along the way that weren't explicitly requested — usually in service of the goal, but not always what you'd have chosen.
How to reduce unwanted additions:
- Be explicit about scope: "Only do X. Do not add, remove, or suggest anything else."
- Ask it to flag assumptions instead of acting on them silently: "If anything is unclear, ask me before proceeding."
- Request a plan first for complex tasks, so you can approve scope before execution.
- Set output constraints (length, format, "no extra commentary").
In short: for narrow, well-specified tasks, AI usually stays close to the brief. For vague or creative ones, expect it to fill gaps with its own judgment — and it's on you to constrain that if you don't want it.
Cited no sources.
Our reading
prompting-controls-it
Lines we picked as the evidence — the model's words, our selection - Be explicit about scope: "Only do X. Do not add, remove, or suggest anything else."
- for narrow, well-specified tasks, AI usually stays close to the brief
- it's on you to constrain that if you don't want it
OpenAI gpt-5.5-2026-04-23 cited no sourcesSample 1 of 6The model's answer, recorded unedited
It depends on how the AI is designed and how clearly the task is specified.
In general, an AI may do one of three things:
- Follow exactly what you asked
- Best for clear, narrow instructions.
- Example: “Rewrite this paragraph in simpler language without changing the meaning.”
- Infer what it thinks you want
- If your request is vague, it may fill in gaps.
- Example: “Make this better” could lead it to change tone, structure, length, or emphasis.
- Add suggestions or extra ideas
- Some AI systems are trained to be helpful and may offer improvements beyond the request.
- Example: If you ask for a title, it may give several options plus advice on which one is best.
If you want it to stick strictly to your request, say that explicitly. For example:
> “Do only what I ask. Do not add ideas, suggestions, explanations, or changes beyond the instructions.”
Or:
> “Follow these instructions exactly. If something is unclear, ask me before making assumptions.”
Even then, AI can still occasionally overstep, so for important work it’s best to review the output.
Cited no sources.
Our reading
residual-deviation-risk
Lines we picked as the evidence — the model's words, our selection - If you want it to stick strictly to your request, say that explicitly.
- Even then, AI can still occasionally overstep, so for important work it's best to review the output.
Sample 2 of 6The model's answer, recorded unedited
It depends on how you ask and what kind of AI system it is.
In general, an AI will try to follow your task, but it may also add its own interpretation unless you constrain it clearly. For example:
- If you say: “Write a marketing email for this product”, it may choose the tone, structure, selling points, and call to action itself.
- If you say: “Rewrite this email only for grammar and clarity. Do not add new claims, examples, or ideas.”, it is much more likely to stay close to your request.
AI often “adds its own ideas” when:
- the instruction is broad or vague,
- it thinks extra context would be helpful,
- it is trying to be creative,
- it is filling in missing details,
- the task normally involves judgment, such as writing, planning, coding, or brainstorming.
If you want it to stick closely to what you asked, use constraints like:
> “Do only the requested task.”
> “Do not add new information.”
> “Do not make assumptions.”
> “Ask me before adding anything not explicitly requested.”
> “Keep the same meaning, tone, and structure.”
> “If information is missing, say what is missing instead of inventing it.”
A good instruction might be:
> “Edit the following text for grammar only. Do not change the meaning, add examples, remove details, or improve the argument. If something is unclear, leave a comment instead of rewriting it.”
So the short answer is: AI usually tries to follow your request, but unless you are specific, it may add interpretation, assumptions, or suggestions.
Cited no sources.
Our reading
prompting-controls-it
Lines we picked as the evidence — the model's words, our selection - It depends on how you ask
- If you want it to stick closely to what you asked, use constraints like
- unless you are specific, it may add interpretation, assumptions, or suggestions
Sample 3 of 6The model's answer, recorded unedited
It depends on how the AI is designed and how you phrase the task.
In general, an AI will try to follow your instructions, but it may also add things if it thinks they are helpful, expected, or implied. For example:
- If you ask: “Summarize this in 3 bullet points.”
It should usually stick closely to 3 bullet points.
- If you ask: “Improve this email.”
It may change wording, tone, structure, and add details unless you say not to.
- If you ask: “Make a plan for my project.”
It will likely add its own suggestions because planning involves judgment.
If you want it to stick strictly to your request, say that clearly. For example:
> “Do only what I ask. Do not add new ideas, assumptions, explanations, or extra content.”
Or:
> “Rewrite this for grammar only. Preserve the meaning exactly. Do not add or remove any information.”
Even then, AI can sometimes misunderstand or over-help, so the best approach is to give clear boundaries and review the output.
Cited no sources.
Our reading
residual-deviation-risk
Lines we picked as the evidence — the model's words, our selection - If you want it to stick strictly to your request, say that clearly.
- Even then, AI can sometimes misunderstand or over-help, so the best approach is to give clear boundaries and review the output.
- it may also add things if it thinks they are helpful, expected, or implied
Sample 4 of 6The model's answer, recorded unedited
It depends on how you frame the task and what kind of AI system you’re using.
In general, an AI will try to follow your request, but it may also add details, assumptions, or suggestions if it thinks they are helpful. This is often called being “helpful,” but it can feel like the AI is going beyond what you asked.
For example:
- If you say: “Rewrite this paragraph.”
It may improve wording, change tone, or restructure sentences.
- If you say: “Rewrite this paragraph without adding new information.”
It is more likely to stay close to the original.
- If you say: “Answer only with yes or no.”
It should usually obey, though not always perfectly.
- If you say: “Make a list of 10 ideas.”
It will generate its own ideas because that’s part of the task.
AI systems often infer intent. If your instruction is broad, they may fill in gaps. If your instruction is narrow, they are more likely to stick to it.
If you want it to avoid adding its own ideas, be explicit. For example:
> “Do not add new facts, examples, opinions, or suggestions. Only use the information I provide.”
Or:
> “Follow my instructions exactly. If something is unclear, ask me a question instead of assuming.”
Even then, AI can still make mistakes, so for important tasks it’s best to review the output carefully.
Cited no sources.
Our reading
prompting-controls-it
Lines we picked as the evidence — the model's words, our selection - It depends on how you frame the task
- If your instruction is broad, they may fill in gaps. If your instruction is narrow, they are more likely to stick to it.
- If you want it to avoid adding its own ideas, be explicit.
Sample 5 of 6The model's answer, recorded unedited
Usually, an AI will try to stick to what you asked, but it may also add its own assumptions, suggestions, or “helpful” extras unless you make your instructions very explicit.
In practice:
- If your request is clear and constrained, it is more likely to follow it closely.
- If your request is vague, it may fill in gaps with its own interpretation.
- If it thinks something would be useful, it may add context, alternatives, warnings, or improvements.
- If there are safety, policy, or factual concerns, it may refuse, modify, or qualify the answer.
- If you ask for creativity, it may take more initiative.
For example, if you say:
> “Write a short email to reschedule my meeting.”
It might add polite phrases, propose a reason, or include suggested times.
If you say:
> “Write exactly two sentences. Do not add a reason. Do not suggest new times.”
It is much more likely to stay within those limits.
A good way to control this is to include instructions like:
> “Do only the task I requested. Do not add suggestions, alternatives, explanations, or assumptions unless I ask for them.”
Or:
> “If information is missing, ask me a question instead of making something up.”
So the short answer is: AI generally follows your task, but unless you constrain it, it may add its own interpretation or extra help.
Cited no sources.
Our reading
prompting-controls-it
Lines we picked as the evidence — the model's words, our selection - it may also add its own assumptions, suggestions, or "helpful" extras unless you make your instructions very explicit
- A good way to control this is to include instructions like: "Do only the task I requested..."
- It is much more likely to stay within those limits.
Sample 6 of 6The model's answer, recorded unedited
Usually, an AI will try to stick to what you asked, but it may also add its own assumptions, suggestions, or “helpful” extras unless you clearly constrain it.
AI systems often infer things like:
- what you probably meant
- what format would be useful
- what details might be missing
- what a “better” answer might include
- common best practices related to the task
That can be useful, but it can also be annoying if you only wanted a narrow answer.
For example, if you ask:
> “Rewrite this paragraph.”
The AI might not only rewrite it, but also make it more formal, shorten it, add emphasis, or change the tone — unless you specify otherwise.
If you want it to stick closely to your request, say things like:
> “Do only what I ask. Do not add suggestions, explanations, or extra content.”
or:
> “Preserve the original meaning exactly. Do not introduce new ideas.”
or:
> “If something is unclear, ask me before making assumptions.”
A good prompt might be:
> “Edit the following text for grammar only. Do not change the wording, tone, structure, or meaning. Do not add new ideas.”
So the short answer is: AI generally follows your instructions, but it may add its own ideas unless you explicitly tell it not to.
Cited no sources.
Our reading
prompting-controls-it
Lines we picked as the evidence — the model's words, our selection - it may also add its own assumptions, suggestions, or "helpful" extras unless you clearly constrain it
- If you want it to stick closely to your request, say things like: "Do only what I ask..."
- AI generally follows your instructions, but it may add its own ideas unless you explicitly tell it not to.
Google gemini-3.6-flash cited no sourcesSample 1 of 6The model's answer, recorded unedited
Whether an AI sticks strictly to your instructions or adds extra ideas depends on how you prompt it, the type of task, and the AI’s underlying settings.
Here is a breakdown of why AI behaves this way and how you can control it:
---
1. Default Behavior: AI Tries to Be "Helpful"
By default, most modern conversational AIs are trained to be as helpful and thorough as possible. Because of this:
- It often extrapolates: If you ask for a simple summary, it might add next steps, context, or suggestions you didn't explicitly request.
- It fills in gaps: If your prompt is vague, the AI will make reasonable assumptions to complete the task rather than asking clarifying questions.
---
2. Why AI "Adds Its Own Ideas"
AI doesn't actually have personal opinions, consciousness, or intent. When it adds unrequested ideas, it's doing two things:
- Predicting plausible text: AI models are statistical word predictors. If a prompt looks like the beginning of a conversation that usually includes advice or extra context, the AI naturally generates that text.
- Following alignment training: AI developers train models using Reinforcement Learning from Human Feedback (RLHF). Human testers tend to reward answers that are detailed and proactive, so the AI learns that adding value beyond the minimum is usually desired.
---
3. How to Make AI Stick Strictly to What You Asked
If you want the AI to follow your prompt like a literal computer program without adding embellishments or unsolicited ideas, use these strategies:
A. Use Explicit Negative Constraints
Tell the AI what not to do.
- Loose prompt: "Summarize this article."
- Strict prompt: "Summarize this article in 3 bullet points. Do not add any outside information, commentary, introductory text, or concluding statements."
B. Define a Rigid Output Format
Force the AI into a specific structure (like JSON, CSV, or a strict template).
- Example: "Extract the dates and event names from this text. Output ONLY a valid JSON array. Do not include markdown formatting or conversational text."
C. Lower the "Temperature" (If using API or custom settings)
In AI settings, temperature controls randomness:
- High Temperature (0.7 – 1.0): Creative, varied, and more likely to branch out or add ideas.
- Low Temperature (0.0 – 0.2): Focused, deterministic, and strict about sticking to patterns.
D. Use System Instructions or Personas
Give the AI a role that demands strict adherence.
- Example: "You are a strict data parser. Your only job is to return exact matches from the text provided. Never offer suggestions, opinions, or extra text."
---
Summary Checklist
| Task Type | Does AI usually add ideas? | What you should do |
| :--- | :--- | :--- |
| Brainstorming & Writing | Yes (and that's usually good) | Let the AI expand, then refine. |
| Data Extraction & Coding | Sometimes (can break code/data) | Use strict formatting guidelines and negative constraints. |
| Analysis & Summarization | Often (may add context/opinions) | Specify "Use ONLY the provided text." |
Cited no sources.
Our reading
prompting-controls-it
Lines we picked as the evidence — the model's words, our selection - Whether an AI sticks strictly to your instructions or adds extra ideas depends on how you prompt it
- If you want the AI to follow your prompt like a literal computer program without adding embellishments or unsolicited ideas, use these strategies
- Use strict formatting guidelines and negative constraints.
Sample 2 of 6The model's answer, recorded unedited
Whether an AI sticks strictly to your instructions or adds its own ideas depends on how you phrase your prompt, the model’s settings, and how it was trained.
By default, most modern conversational AIs are trained to be as helpful as possible, which means they often lean toward elaborating or offering extra suggestions unless you explicitly tell them not to.
Here is a breakdown of why this happens and how you can control it:
---
Why an AI Might Add Its Own Ideas
- It's trained to be helpful: AI models undergo training (such as Reinforcement Learning from Human Feedback) that rewards them for providing thorough, conversational, and helpful answers. If your prompt leaves room for interpretation, the AI will try to "fill in the blanks" or anticipate what you might need next.
- Temperature and Randomness Settings: AI doesn't think; it calculates probabilities of what word should come next. A setting called "Temperature" controls how creative or predictable those choices are:
- High Temperature: Makes the AI more creative, varied, and likely to introduce novel ideas.
- Low Temperature: Makes the AI more strict, logical, and focused strictly on the input data.
- Open-Ended Prompts: If you ask, "Write a recipe for pasta," the AI will add its own ideas (which sauce, which herbs, cooking tips). If you ask, "List only the 3 main steps to boil dry pasta, with no extra commentary," it will stick to the exact task.
---
How to Make an AI Stick Strictly to What You Asked
If you want the AI to act like a precise machine rather than a creative partner, use these prompt techniques:
- Set Explicit Boundaries: Tell the AI what not to do.
- Example: "Summarize this article in three bullet points. Do not add any outside information, extra advice, or conversational text."
- Specify Output Formats: Force the AI into a strict container.
- Example: "Translate the following sentences into Spanish. Output only a JSON object containing the translations and nothing else."
- Constrain the Source Material: Limit the AI to information you provide.
- Example: "Answer the question using only the text provided below. If the answer is not in the text, say 'Information not available.'"
---
How to Encourage the AI to Add Ideas
If you want the AI to act as a brainstorm partner or offer fresh perspectives:
- Use Open-Ended Phrasing: Invite suggestions.
- Example: "Critique my business plan and suggest 5 innovative features I haven't thought of."
- Assign a Persona: Give it a creative role.
- Example: "Act as an experienced marketer. Look at this ad copy and rewrite it with 3 different creative angles."
---
Summary
The AI doesn't have personal opinions or true "ideas" of its own—it generates text based on patterns in its training data. You have full control over the result: if you give it loose instructions, it will improvise; if you set tight boundaries, it will follow them to the letter.
Cited no sources.
Our reading
prompting-controls-it
Lines we picked as the evidence — the model's words, our selection - depends on how you phrase your prompt
- You have full control over the result: if you give it loose instructions, it will improvise; if you set tight boundaries, it will follow them to the letter.
- it will stick to the exact task
Sample 3 of 6The model's answer, recorded unedited
Whether an AI strictly sticks to your instructions or adds extra details/ideas depends on how you phrase your prompt and the type of task you give it.
Here is a breakdown of how AI behaves and why:
---
1. Does AI actually have "its own ideas"?
First, it helps to know that AI does not have personal opinions, desires, or true creativity. What seems like "its own ideas" is actually the AI predicting patterns and synthesizing information from the massive dataset it was trained on.
---
2. When will the AI add extra details or "ideas"?
An AI will often go beyond a simple request in these situations:
- Vague or Open-Ended Prompts: If you ask, "How do I plan a birthday party?", the AI has to fill in the blanks. It will invent themes, schedules, and guest list ideas to give you a complete answer.
- "Helpfulness Bias": AI models are trained to be as helpful and thorough as possible. Because of this, even if you ask a straightforward technical question, the AI might add unprompted warnings, best practices, or follow-up suggestions (e.g., "Here is the Python code you asked for. By the way, make sure you install this library first...").
- Creative Tasks: If you ask it to write a story, draft an email, or brainstorm, it will naturally generate original twists, phrasing, or concepts based on context.
- Hallucination (Making things up): If the AI lacks factual information to answer your request, it might invent facts or details to fill the gap rather than admit it doesn't know (though modern models are getting better at avoiding this).
---
3. Can you force the AI to stick only to what you asked?
Yes. You can strictly control how closely the AI follows your instructions through your prompt design.
- Use strict constraints: Tell the AI explicitly what not to do.
- Example: "Summarize the text below in exactly 3 bullet points. Do not add any introductory text, concluding remarks, or outside knowledge."
- Define the format: Specify exact formats (e.g., "Output the result purely as a JSON object" or "Answer in a single word: Yes or No").
- Specify the role: Tell it how strictly to act (e.g., "Act as a strict proofreader. Only correct spelling and grammar errors. Do not rewrite sentences or offer style suggestions.")
---
Summary Guidelines
| If you want the AI to... | How to prompt it |
| :--- | :--- |
| Stick strictly to your input | "Use ONLY the provided text to answer. Do not add commentary, assumptions, or extra suggestions." |
| Bring in extra ideas/creativity | "Give me a basic outline, but feel free to suggest creative variations or unexpected angles I haven't thought of." |
Cited no sources.
Our reading
prompting-controls-it
Lines we picked as the evidence — the model's words, our selection - depends on how you phrase your prompt and the type of task you give it
- Yes. You can strictly control how closely the AI follows your instructions through your prompt design.
- "Use ONLY the provided text to answer. Do not add commentary, assumptions, or extra suggestions."
Sample 4 of 6The model's answer, recorded unedited
Whether an AI sticks strictly to what you asked or adds its own ideas depends on how you phrase your prompt, the nature of the task, and the settings of the AI model.
Here is a breakdown of how AI behaves and how you can control it:
---
1. AI Doesn’t Have "Its Own Ideas" (It Fills Patterns)
First, it helps to understand that AI doesn't have personal preferences, beliefs, or genuine originality. It generates text by predicting what comes next based on patterns in its training data.
- If a task leaves room for interpretation, the AI will fill in those blanks using common patterns, which can look like it’s adding its own ideas.
- If a task is tightly constrained, the AI will focus narrowly on following your rules.
---
2. When Will an AI Add Extra Ideas?
An AI is likely to expand, suggest, or improvise when:
- Your prompt is open-ended or ambiguous: If you ask, "Write an essay about renewable energy," the AI has to decide on the structure, tone, subtopics, and examples by itself.
- The task invites creativity: Asking for brainstorming, writing, or problem-solving signals to the AI that you want suggestions beyond what you provided.
- The "Temperature" setting is high: In developer settings or advanced platforms, a parameter called temperature controls randomness. Higher temperature = more creative/unpredictable; lower temperature = more logical/strict.
---
3. When Will an AI Stick Strictly to Instructions?
An AI will stick closely to your request when:
- You set strict boundaries/constraints: Asking it to "Summarize the text below using only the provided facts, without adding extra context" will keep it focused.
- The task is highly structured: Formatting requests (e.g., "Convert this paragraph into a JSON object with keys 'date' and 'location'") naturally leave little room for extrapolation.
---
How to Get the Result You Want
If you want strict adherence (No extra fluff/ideas):
- Use explicit negative constraints: Tell it what not to do (e.g., "Do not include an introduction or conclusion," or "Rely only on the provided text. Do not bring in outside knowledge.").
- Provide an exact output format: Give a template or example of how you want the response structured.
- Limit scope: Define exact limits, like word counts or specific bullet points.
If you want creative input and new ideas:
- Ask for options or variations: "Give me 5 different creative angles for..."
- Encourage extrapolation: "What are some unexpected edge cases or missing points I haven't considered?"
- Give it a role: "Act as a visionary marketing strategist and suggest..."
Cited no sources.
Our reading
prompting-controls-it
Lines we picked as the evidence — the model's words, our selection - depends on how you phrase your prompt, the nature of the task, and the settings
- If a task is tightly constrained, the AI will focus narrowly on following your rules.
- Use explicit negative constraints: Tell it what not to do
Sample 5 of 6The model's answer, recorded unedited
Whether an AI sticks strictly to what you ask or adds its own ideas depends on how you phrase your prompt, the nature of the task, and the AI’s underlying settings.
Here is a breakdown of how AI behaves and how you can control it:
---
1. By default, AI tends to "fill in the gaps"
Large Language Models (LLMs) are pattern-prediction engines. When you give them a prompt, they look for the most logical, useful, or coherent continuation based on their training data.
- If your prompt is open-ended or brief (e.g., "Write a strategy for launching a coffee shop"), the AI will add its own ideas, details, and assumptions to give you a complete answer.
- It isn't generating human "desires" or "opinions," but rather drawing on common concepts, structures, and creative elements from its training data to complete the task thoroughly.
---
2. When will an AI add its own ideas?
An AI is more likely to improvise or introduce unasked-for information if:
- The prompt is vague: Lacking specific constraints forces the AI to guess what context you need.
- The task is creative: Prompts asking for stories, brainstorming, or marketing copy naturally invite the AI to draw upon diverse concepts.
- High "Temperature" setting: In tools where you can adjust settings, a higher "temperature" makes the output more creative and unpredictable, while a lower temperature makes it rigid and formulaic.
- Helpfulness bias: AIs are trained to be as helpful as possible. If it thinks a piece of advice or extra context would help answer your question, it will often include it even if you didn't explicitly request it.
---
3. How to make the AI stick strictly to your instructions
If you want the AI to adhere only to what you provided without adding unprompted ideas, extra facts, or unnecessary fluff, you can use specific prompting techniques:
- Use strict negative constraints:
- "Answer the question using ONLY the provided text below. Do not assume or extrapolate any information not directly mentioned."
- "Do not include an introduction, conclusion, or conversational filler."
- Specify the format and boundaries:
- "Output the result strictly as a JSON object with keys 'name' and 'date'."
- "Summarize this article in exactly 3 bullet points, using only facts directly stated in the text."
- Provide examples ("Few-Shot Prompting"):
- Give the AI one or two examples of input and exact expected output so it understands the rigid boundaries of the task.
- Set the persona/role:
- "Act as a strict data extraction tool. You have no creative capabilities. Translate the following text directly into..."
---
Summary
- Without strict instructions: AI will act as a collaborator, filling gaps, elaborating, and adding related ideas.
- With strict instructions: AI can act as a rigid tool, following precise rules and ignoring outside concepts.
Cited no sources.
Our reading
prompting-controls-it
Lines we picked as the evidence — the model's words, our selection - depends on how you phrase your prompt, the nature of the task, and the AI's underlying settings
- If you want the AI to adhere only to what you provided ... you can use specific prompting techniques
- With strict instructions: AI can act as a rigid tool, following precise rules and ignoring outside concepts.
Sample 6 of 6The model's answer, recorded unedited
The short answer is: it depends on how you write your prompt, but by default, an AI will often expand, assume, or add its own ideas unless you strictly forbid it to.
Here is a breakdown of why this happens and how you can control it:
---
Why AI Might Add Its Own Ideas
- The "Helpfulness" Bias
AI models are trained to be helpful assistants. If you ask a broad or simple question, the AI often anticipates what you might want next. For example, if you ask "How do I bake bread?", it won't just give you a 2-step answer; it will likely add equipment lists, tips on yeast, and troubleshooting advice.
- Pattern Completion
Large Language Models (LLMs) work by predicting the most statistically likely text to follow your input. If your prompt leaves open gaps, the AI fills those gaps using patterns learned from its training data.
- Ambiguity
If a prompt is vague, the AI has to make assumptions. If you ask it to "Write a story about a detective," it will invent the detective’s name, setting, plot, and personality entirely on its own.
- Hallucination vs. Creativity
Sometimes an AI adds details not because it's trying to be creative, but because it is misinterpreting facts or making up plausible-sounding information (known as hallucination).
---
When AI Sticks Strictly to What You Asked
An AI will stick tightly to your task when:
- The task is highly structured: E.g., "Translate this paragraph into Spanish" or "Convert this JSON data to a CSV table."
- You explicitly place strict constraints on it: E.g., "Summarize this text in exactly 3 bullet points. Do not add any introduction, conclusion, or outside facts."
---
How to Make an AI Stick ONLY to Your Request
If you want the AI to be a strict executor without adding extra flair or unsolicited ideas, use these prompting techniques:
- Use Explicit Boundaries and Negative Constraints
- Example: "Answer the question using only the provided text. Do not draw on outside knowledge. If the answer is not in the text, say 'I don't know'."
- Restrict the Output Format
- Example: "Provide your answer strictly as a raw list of numbers. Do not include any conversational filler, greetings, or explanations."
- Lower the "Temperature" (If using API or advanced settings)
- In AI settings, temperature controls randomness. A temperature of
0.0makes the AI deterministic and focused strictly on the most direct answer, while a higher temperature (like0.8or1.0) encourages creativity and variation.
---
Summary
- Without strict limits: The AI acts like a collaborative partner—it will offer extra suggestions, elaborate, and fill in missing details.
- With strict limits: The AI acts like a precise tool—it will follow your rules to the letter and ignore everything else.
Cited no sources.
Our reading
prompting-controls-it
Lines we picked as the evidence — the model's words, our selection - it depends on how you write your prompt, but by default, an AI will often expand, assume, or add its own ideas unless you strictly forbid it to
- With strict limits: The AI acts like a precise tool—it will follow your rules to the letter
- Use Explicit Boundaries and Negative Constraints
Grok grok-4.6 cited sources in 3 of 6, 11 in totalSample 1 of 6The model's answer, recorded unedited
It depends on the AI, how you phrase the task, and the model's training—but most will not stick strictly to the letter of your request. They frequently interpret intent, add extra context, suggestions, explanations, or "helpful" extras even when you didn't ask for them.
Modern large language models are trained to be helpful and to complete patterns from huge amounts of data. That often means they:
- Infer what you probably wanted and expand on it (e.g., you ask for a Python function and they also add usage examples, comments, and a warning about edge cases).
- Add unsolicited ideas, alternatives, or disclaimers because their training rewards being thorough or "maximally useful."
- Sometimes go off-script due to sycophancy, over-helpfulness, or just generating more than requested.
You can reduce this by being very explicit: "Do exactly this and nothing else. No extra text, no suggestions, no explanations." Many models will then follow more closely, especially stronger recent ones. Even then, they aren't perfect executors—they're probabilistic generators.
Different AIs vary:
- Some (certain ChatGPT-style models) tend to be more verbose and add extras by default.
- Others can be steered toward being more literal or concise.
- "Creative" or high-temperature settings increase the chance of extra ideas.
If you want the AI to stay tightly on-task, treat prompting as a skill: specify constraints clearly, give examples of the exact output format you want, and iterate. The more ambiguous or open-ended the task, the more the model will fill in its own ideas.
In short: AIs are not mindless slaves to your exact words. They're more like very knowledgeable collaborators who often can't resist adding a little extra. Clear instructions help a lot, but they rarely behave like a simple script.
Cited no sources.
Our reading
residual-deviation-risk
Lines we picked as the evidence — the model's words, our selection - most will not stick strictly to the letter of your request
- Even then, they aren't perfect executors—they're probabilistic generators.
- Clear instructions help a lot, but they rarely behave like a simple script.
Sample 2 of 6The model's answer, recorded unedited
It depends on the AI, the prompt, and how the model is trained—but most of the time they will add extra ideas, explanations, or “improvements” unless you tightly constrain them.
Large language models (including me) are trained to be helpful, not to be literal robots that only output exactly what you asked and nothing more. That training (especially RLHF/alignment) creates a strong bias toward:
- Adding context, caveats, or extra suggestions
- Over-explaining
- Trying to anticipate what you might want next
- Softening or expanding the request in ways the model thinks is better
When it usually sticks close
- Very specific, constrained tasks (“Write a Python function that does X, no comments, no extra functions”)
- Explicit instructions like “Do only this. Do not add anything else.” or “Output only the requested text.”
- Short, mechanical tasks (formatting, translation of a short string, simple calculations)
When it commonly adds its own ideas
- Open-ended or creative tasks (writing, brainstorming, coding a feature)
- Vague prompts
- Anything that looks like it “could be better” (the model often injects extra error handling, comments, features, or moral/safety notes)
- Conversational style — many models treat the interaction as a dialogue rather than a strict command
This is why people often have to add phrases like “Do not add extra commentary,” “Just the code,” or “Follow the instructions exactly.” Even then, models sometimes still sneak things in.
Bottom line: Current AIs are not perfectly obedient executors. They’re more like very knowledgeable, slightly over-eager assistants that frequently decide to “help extra.” If you want strict adherence, you have to be unusually explicit and often iterate. If you want creativity and extra ideas, most models will happily supply them (sometimes more than you wanted).
Cited no sources.
Our reading
residual-deviation-risk
Lines we picked as the evidence — the model's words, our selection - most of the time they will add extra ideas, explanations, or "improvements" unless you tightly constrain them
- Even then, models sometimes still sneak things in.
- Current AIs are not perfectly obedient executors.
Sample 3 of 6The model's answer, recorded unedited
It depends on the AI, how you phrase the task, and the context, but they frequently add extra ideas, explanations, suggestions, or content you didn't request. Most modern AIs (like ChatGPT, Claude, Gemini, or Grok) are trained to be "helpful," which often means going beyond a strict, literal interpretation of your prompt. They complete patterns from their training data, fill in perceived gaps, and follow hidden system-level instructions that push them toward being comprehensive, proactive, or conversational.[[1]](https://www.washingtonpost.com/technology/interactive/2026/chatbots-hidden-rules-system-prompts/)
They are explicitly trained (via techniques like RLHF) to follow user instructions better than raw language models, but this isn't perfect. Studies and real-world use show they often:
- Add unsolicited caveats, disclaimers, related suggestions, or extra context because their default is to be useful rather than minimally obedient.
- Override or loosely interpret style/format constraints (e.g., adding extra commentary when you asked for a short list, or defaulting to a certain tone).
- Fill in assumptions if your prompt is vague—deciding details like budget, audience, or next steps on their own instead of asking or stopping.[[2]](https://www.xda-developers.com/added-single-line-to-chatgpt-prompt-difference-was-immediate/)
- Drift over longer conversations, especially if later messages create conflicting patterns or if they over-condition on their own previous replies. Instruction-following rates drop in multi-turn chats.[[3]](https://arxiv.org/html/2410.15553v2)
Newer models are more capable of inferring intent and being proactive (OpenAI even advises against over-specifying every step now), which can feel like them "adding their own ideas." They're also influenced by massive hidden system prompts that companies add behind the scenes—thousands of words telling them things like "be helpful and accessible," avoid certain phrases, respect copyright, or adopt a personality. These often take priority and can override or expand on what you asked.[[4]](https://www.xda-developers.com/stripped-my-prompts-down-after-openais-new-guide/)
Instruction-following is brittle. Even advanced models fail simple constraints (word limits, exact formats, "don't add X") more often than you'd expect, especially if the request conflicts with their training priors, involves long/multi-turn chats, or uses slightly rephrased wording. They can also exhibit sycophancy (agreeing or expanding in ways that please you) or "context pollution" from earlier outputs. Some research highlights that they don't always prioritize user instructions over internal patterns or system rules.[[5]](https://artoftruth.org/llm-instruction-following-compliance-gap/)
How to get closer to "just do exactly what I asked"
- Be extremely specific and explicit: "Do only this. Do not add explanations, suggestions, extra ideas, or related topics. Output exactly [format]." Repeat key constraints.
- Use custom/persistent instructions (available in many chat interfaces) to set defaults like "be concise, no unsolicited advice."
- Test and iterate: Start lean rather than piling on rules. Some models perform better with stripped-down prompts.
- For high-stakes or precise tasks, specify "stick strictly to the request" or break it into steps. If it's creative, they will add ideas—that's often the point.
- Different models vary: Some are more verbose or "helpful" by default; others (or specific versions) can be steered toward tighter adherence. Temperature/settings also affect creativity vs. strictness.
In short, treat them as collaborative tools that interpret and expand rather than perfect executors. If you want zero extras, you'll usually need to constrain them tightly—and even then, they might slip. If the task is open-ended (writing, brainstorming, analysis), adding ideas is a feature, not a bug.
5 sources the model cited
- https://www.washingtonpost.com/technology/interactive/2026/chatbots-hidden-rules-system-pr
- https://www.xda-developers.com/added-single-line-to-chatgpt-prompt-difference-was-immediat
- https://arxiv.org/html/2410.15553v2
- https://www.xda-developers.com/stripped-my-prompts-down-after-openais-new-guide/
- https://artoftruth.org/llm-instruction-following-compliance-gap/
Our reading
residual-deviation-risk
Lines we picked as the evidence — the model's words, our selection - Instruction-following is brittle. Even advanced models fail simple constraints (word limits, exact formats, "don't add X") more often than you'd expect
- If you want zero extras, you'll usually need to constrain them tightly—and even then, they might slip.
- they frequently add extra ideas, explanations, suggestions, or content you didn't request
Sample 4 of 6The model's answer, recorded unedited
It depends on the AI, how you phrase the task, and the model’s training/alignment—but most modern AIs (including me) try to follow your instructions closely while often adding extra value.
Instruction-tuned models are explicitly trained to do what you ask. They parse your request, generate a relevant response, and aim to stay on-topic. If you say “write a 3-sentence summary of X” or “just give me the list, nothing else,” a well-behaved model will usually comply.
In practice, they frequently add their own ideas or extra content because:
- They’re optimized to be helpful, not just literal. Extra context, examples, caveats, related suggestions, or a bit of personality often get included if the model judges it useful.
- They generate probabilistically from training data, so they can interpolate, analogize, or surface related knowledge.
- System-level instructions (be truthful, be witty, be maximally helpful, avoid certain harms) influence the output.
- Vague or open-ended prompts invite more creativity or “helpful extras.”
You’ll see this as:
- Unsolicited tips, explanations, or “you might also consider…”
- Humor, analogies, or a distinctive voice (I do this as Grok).
- Safety-related additions or refusals when the request hits a boundary.
- Occasional over-helpfulness or slight deviation if the instruction is complex or ambiguous.
You can reduce extras by being very specific (“only output the JSON, no commentary,” “do not add suggestions”). Even then, perfect adherence isn’t guaranteed—models still sometimes hallucinate, misinterpret, or layer in trained behaviors.
Bottom line: A good AI will stick to the core of what you asked far more often than not, but it isn’t a mindless executor. It has a trained tendency to be useful, which frequently means adding a bit of its own framing or extra information. If you want strict adherence, state it clearly in the prompt.
Cited no sources.
Our reading
residual-deviation-risk
Lines we picked as the evidence — the model's words, our selection - You can reduce extras by being very specific... Even then, perfect adherence isn't guaranteed—models still sometimes hallucinate, misinterpret, or layer in trained behaviors.
- it isn't a mindless executor. It has a trained tendency to be useful, which frequently means adding a bit of its own framing or extra information
Sample 5 of 6The model's answer, recorded unedited
It depends on the specific AI, how clearly you phrase the task, and whether the request is open-ended or tightly constrained—but most modern AIs will often add extra ideas, explanations, suggestions, or follow-ups rather than sticking only to what you asked. They’re trained to be helpful assistants, not purely mechanical executors.[[1]](https://openai.com/index/instruction-following/)
Models like ChatGPT, Claude, Gemini, and Grok are fine-tuned (via instruction tuning and techniques like RLHF) to understand and follow user intent far better than earlier base models. This makes them generally good at completing the core task you describe. However, “helpful” training frequently leads them to go beyond the request: they add context, warnings, alternative approaches, extra details for completeness, or even unsolicited follow-up questions (“Want me to also…?”). Users commonly have to add explicit prompts like “skip the closing offers and sign-off questions” or use custom/persistent instructions to suppress this.[[2]](https://www.pcworld.com/article/3221302/i-got-chatgpt-to-stop-its-annoying-follow-up-questions-with-one-prompt.html)
Instruction-following is also imperfect in practice. Research highlights an “instruction gap”: models can excel at general capabilities yet still miss precise constraints (format, length, exact wording, “do not add X”). Performance varies widely across models, degrades over multi-turn conversations (they can “drift” or start following later patterns instead of the original request), and can be overridden by in-context examples or competing signals. Some models resist extra content better than others depending on the prompt style.[[3]](https://arxiv.org/html/2601.03269v1)
Different AIs have distinct tendencies:
- Claude often treats prompts like a specification and follows constraints more faithfully.
- ChatGPT tends to be professional and proactive.
- Grok (xAI) can be more direct/casual with a stronger personality that sometimes overrides tone instructions.
To get closer to “stick strictly to what I asked”:
- Be extremely specific and use delimiters, bullet points, or XML-style structure (“Output only this format. Do not add explanations, extra sections, or suggestions.”).
- Repeat key constraints.
- Use custom/system instructions for recurring rules.
- Test and iterate, as even well-trained models aren’t 100% reliable on every edge case.
If your task is creative or underspecified, they’ll fill in gaps with their own (trained) ideas. If you want zero extras, say so explicitly—otherwise expect some helpfulness baked in.
Our reading
residual-deviation-risk
Lines we picked as the evidence — the model's words, our selection - Instruction-following is also imperfect in practice. Research highlights an "instruction gap"
- Test and iterate, as even well-trained models aren't 100% reliable on every edge case.
- If you want zero extras, say so explicitly—otherwise expect some helpfulness baked in.
Sample 6 of 6The model's answer, recorded unedited
It depends on the AI, how you phrase the task, and what "stick to it" means—but most current AIs (large language models) will often add extra ideas, context, explanations, or related suggestions rather than delivering a purely minimal response. They don't have independent "ideas" like a human; they generate likely continuations based on training data and post-training for helpfulness. This frequently leads to going beyond the exact ask.[[1]](https://www.science.org/doi/10.1126/science.aea3922)
AIs are optimized (via techniques like RLHF) to be useful, polite, and comprehensive. This can produce unsolicited elaboration, extra background, related tips, or even slight reinterpretations of your intent—especially if the prompt is even a little ambiguous. They may default to their training patterns instead of asking for clarification. Models can also blend in knowledge from their training even when you want them to use only what you provided. Strict rule-following holds up reasonably well for simple, short instructions but degrades as prompts get longer or more constrained.[[2]](https://www.linkedin.com/pulse/llms-follow-rules-until-dont-alan-roth-kgwxc)
In creative or open-ended tasks, they tend to remix common patterns from training data rather than inventing truly novel ideas, and aligned ("helpful") versions often favor pleasant, conventional, or comprehensive output over pure originality or minimalism. Different models vary: some (like certain comparisons of Grok) follow instructions more literally, while others add more commentary or refuse/adjust based on their alignment. None are perfectly consistent.[[3]](https://arxiv.org/html/2505.00047)
To make an AI stick closer to exactly what you asked:
- Be extremely explicit: "Respond only with [exact format/output]. Do not add explanations, extra ideas, context, suggestions, or anything else."
- Add constraints like "no extra information," "be concise," or "do not go beyond the requested task."
- Provide examples of the desired (minimal) output.
- Test and iterate—behavior isn't 100% reliable across models or even the same model on repeated runs.
If you want extra ideas, you can explicitly invite them ("include related suggestions if relevant"). The more precise and constrained your prompt, the closer the result usually gets, but there's no absolute guarantee because these systems predict text rather than execute rigid programs. If you're using a specific AI, try it with a tight prompt and see.
Our reading
residual-deviation-risk
Lines we picked as the evidence — the model's words, our selection - The more precise and constrained your prompt, the closer the result usually gets, but there's no absolute guarantee
- Test and iterate—behavior isn't 100% reliable across models or even the same model on repeated runs.
- most current AIs (large language models) will often add extra ideas, context, explanations, or related suggestions
The positions for this question
Fixed before the run, so answers can move between them without the scale being redrawn. A position nothing landed on is still listed: it is the room the answers have to move into.
-
prompting-controls-it Anthropic 6, OpenAI 4, Google 6
Concludes that the behavior is fully steerable by the user: with vague prompts the AI will extrapolate and add ideas, but if you state explicit constraints ('do only X, add nothing else') it will reliably stick to what you asked. Gives no meaningful caveat that it may still deviate despite clear instructions, and does not urge routine output review as a safeguard.
-
residual-deviation-risk OpenAI 2, Grok 6
Concludes that explicit instructions help but are not sufficient: the reader should assume the AI may still add unrequested content, make assumptions, or overstep scope even when told not to, and should therefore verify/review output or otherwise plan for deviation. Distinguished from 'prompting-controls-it' by asserting leftover unpredictability after clear constraints are given.
-
strict-by-default no samples
Concludes that AI will by default do essentially just what was asked and not inject its own ideas — the main risk being excessive literalism or under-delivery — so the reader should prompt for extra suggestions or judgment if they want them, rather than guarding against scope creep.
-
no-usable-answer no samples
Gives no actionable bottom line: either refuses, answers a different question, or discusses AI behavior so noncommittally that the reader learns neither what to expect by default nor what to do about it (no guidance on constraining prompts or on reviewing output).