Quick answer
The fix for generic AI output is how to give AI context in three separate blocks rather than one sentence: Frame (your role and reader), Fence (tone, length, structure, and what to avoid), and Feed (the real dates, figures and names). Stack all three above your task line.
You write a clear request. The AI writes back something that could have been sent to anyone, at any company, in any industry. Learning how to give AI context is the whole difference between that paragraph and a draft you can actually send — and most people are already giving context. Just not in a form the model can act on.
The problem is rarely that you said too little. It’s that everything arrived as one undifferentiated blob — who you are, what you want, the rules, and the actual facts, all mashed into a single sentence, with nothing marking which part is a hard rule. Three kinds of information do three different jobs inside a request:
- Situation — who you are, who reads this, why it matters right now.
- Rules — tone, length, structure, and what to avoid.
- Facts — the dates, numbers, names, and source text only you have.
Separate those three and the output changes on the first try. That separation is what this guide teaches, and it works the same way in ChatGPT, Claude, Gemini, or Copilot.
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Why AI Output Comes Back Generic
A language model fills every gap you leave. When your request doesn’t say who the reader is, it writes for an average reader. When it doesn’t say how long, it writes an average length. Generic output isn’t the model failing — it’s the model averaging, because averaging is all that’s left when the specifics are missing.
The same request, sent two ways: the gaps you leave are the gaps the model fills with averages.
Here is the same client email request, sent both ways. The difference is not effort — the second version took about ninety extra seconds to type.
✕ Everything in one sentence
"Write an email to my client about the project delay." Result: "Dear Valued Client, We are writing to inform you of an unforeseen delay affecting your project. We sincerely apologise for any inconvenience this may cause and remain committed to delivering excellence…"
✓ Situation, rules and facts separated
Same request, with the reader named, a 180-word cap, a no-bullets rule, and the two real dates supplied. Result: "Hi Priya — the Apex install has slipped from 20 to 28 August. A parts shipment we depend on arrived late…"
Notice what didn’t happen there. Nobody wrote a longer request and nobody used a magic phrase. Knowing how to give AI context is really just knowing which four things to keep apart — and the payoff is that the model stops guessing at the three you left out.
This sits inside the wider Role, Goal, Context, Format structure we use across the site. Context is the element people skip hardest, because it feels like typing things the model “should” already know.
How to Give AI Context in Layers
The short version of how to give AI context: split it into three labelled blocks and stack them above your actual task. We call this the Frame-Fence-Feed Method at PromptPeakAI, and the names are the point — each one answers a question the model would otherwise guess at.
| Layer | What it answers | What breaks without it |
|---|---|---|
| Frame | Who is writing, who is reading | Output reads like a press release |
| Fence | What shape and tone to produce | You rewrite it to fit house style |
| Feed | Which facts are true here | The model invents plausible details |
The anatomy of a context block: three labelled sections, then one sentence telling the model what to make.
Frame: Who and Why
Frame is two sentences, not a biography. Your role, the reader, and the one thing that reader cares about most. “I’m a project manager; this goes to a finance director who wants to know whether the number changed” already rules out half the drafts the model might have written.
If you want to go further on the role side of this, the role prompt formulas cover it in detail. For most workplace writing, naming the reader matters more than naming the persona.
Fence: Rules and Shape
Fence is where you stop rewriting. Tone, word limit, structure, and — the part almost everyone gets wrong — the bans. A ban on its own leaves a hole, and the model fills holes with averages. Pair every ban with its replacement.
✕ Ban with nothing to replace it
"Don't sound corporate."
✓ Ban plus the replacement
"Don't use the words 'exciting', 'robust', or 'best-in-class'. Where you'd reach for one, write the plain equivalent instead."
That pairing echoes OpenAI’s own guidance in its prompt engineering best practices, which recommends saying what to do instead of only saying what not to do. Our deeper guide to negative constraints and the guide to controlling output format both live in this layer.
Feed: The Facts Themselves
Feed is the layer people are most nervous about and most under-supply. Every number, date, name, or quote the output needs has to be in here — because if it isn’t, the model will produce something that reads correct and isn’t. That is the mechanism behind most invented details in AI output.
Add one line at the bottom of Feed and the failure mode changes from silent to visible:
Use only the facts in the FEED section above. If something you need is not there, write [MISSING] and carry on — do not fill the gap with a plausible-sounding guess.
You should now be able to name, for any bad output you've ever received, which of the three layers was thin.
Building Your Context Block Step by Step
Labels matter more than formatting. Writing the words FRAME, FENCE, and FEED on their own lines is enough — OpenAI’s guidance recommends putting instructions and context at the top of the request, separated by a clear marker, and plain capitalised labels do that job as well as any symbol.
Type the three labels first
Open a blank chat and type the four headings before you type anything else. An empty label is a visible prompt to yourself — you will notice that you have nothing to write under FEED, which is exactly the thing you would otherwise have skipped.
Fill Frame and Fence from memory
These two barely change between requests of the same type. Your role is stable. Your house tone is stable. Write them once properly and you are copying them forever.
Paste the facts under Feed
Raw is fine. Bullet fragments, a pasted email thread, a row of figures — the model will tidy it. What it cannot do is invent it.
FRAME I am [your role] at [type of company]. This will be read by [who reads it], who cares most about [their main concern]. FENCE Tone: [e.g. calm and accountable]. Length: under [N] words. Structure: [e.g. three short paragraphs, no bullets]. Do not use [words or claims to avoid] — write [what to write instead] in their place. FEED [The real facts: dates, figures, names, quotes, or pasted source text. Only what is true.] If a fact you need is not above, write [MISSING] rather than guessing. TASK [One sentence: write the thing.]
A ChatGPT or Claude input box containing a prompt with FRAME, FENCE and FEED written as separate labelled lines, just before sending.
Crop to the composer area only. Hide the sidebar, chat history, account name and email address.
File: chatgpt-layered-context-prompt.png · Alt: "Chat input box showing a prompt structured with Frame, Fence and Feed labels"You should now have a four-part block on screen with something written under every label.
Context Blocks for Two Common Tasks
Two filled-in examples, both from work that goes wrong often. Swap the details for yours and the structure holds.
FRAME I am an account manager at a logistics company. This goes to my main contact at a client who is sensitive about deadlines and has been patient so far. FENCE Tone: calm, accountable, specific. Length: under 180 words. Structure: three short paragraphs, no bullet points. Do not apologise more than once. Do not name our supplier — write "a supply issue on our side" instead. FEED Original delivery date: 20 August. New date: 28 August. Cause: a parts shipment we depend on arrived late. What we have already done: our engineering team is working the two weekends in between, at no extra cost to the client. I can offer a call on Thursday afternoon. TASK Write the email to the client.
That one pairs naturally with our walkthrough on writing a client delay notice. The second example is a document rather than a message, and the Fence carries more weight because the output has a required shape.
FRAME I am an operations lead at a 50-person consulting firm. This SOP will be read by new team leads who have never onboarded a contractor before. FENCE Structure: Purpose, Scope, Who does what, Steps, How to check it worked. Tone: plain instructional English, no jargon. Do not invent any step, tool or approval that is not listed below. If something is missing, list it under a final heading called "Gaps to confirm". FEED Tools we actually use: our HR system for the record, an e-signature tool for the contract, a dedicated channel for access requests. Hard rule: tax forms must be signed off before any system access is granted. Final approval sits with the operations lead. TASK Write the contractor onboarding SOP.
Compare what each layer is carrying in those two. In the email, Frame does most of the work — the reader and their sensitivity shape every sentence. In the SOP, Fence carries the required document shape and Feed stops invented approval steps appearing. Same three labels, different weight, which is why the structure survives being moved between tasks.
When AI Ignores Your Constraints
It will happen. The block was right and the output still drifted. Almost every case traces back to one of four causes, and none of them need you to start over.
| What you see | Likely cause | The fix |
|---|---|---|
| Generic corporate voice | No reader named in Frame | Name the reader and their concern |
| Format ignored halfway through | Fence buried under a long Feed | Move the rules above the facts |
| Invented figures or dates | Feed too thin to answer | Add the do-not-invent line |
| Rules obeyed then quietly dropped | Long thread, early context scrolled away | Repaste the Fence with your follow-up |
The last row is the one that catches experienced users. Everything works, you iterate happily for twenty minutes, and somewhere around message eight the tone quietly reverts. Nothing broke — the rules simply scrolled out of the model’s working view. Repasting three lines of Fence alongside your next message is faster than explaining what went wrong.
Read the symptom, fix the layer — three of the four failures are a single missing block.
When you genuinely cannot tell which layer is thin, hand the diagnosis over. This is the one place where asking the model about your own request beats guessing, and it pairs well with the diagnostic approach in our guide to refining AI output without starting over.
Below is a context block I am about to send you. Do not answer it yet. Instead, tell me: 1. Which of the three sections — Frame, Fence, Feed — is thinnest. 2. Any fact you would have to invent in order to complete the task. 3. The two questions you would most need answered to do this well. [paste your context block here]
What Context Cannot Fix
Context is the single biggest improvement most people can make to their AI output. It is not a substitute for the parts of the job that were always yours.
Three things stay with you, and pretending otherwise will cost you:
- A weak decision, framed well. If the underlying call is wrong, a beautifully structured email just delivers it more fluently.
- Judgement about this reader, today. You know whether your client needs warmth or brevity this week. The model is guessing from words on a screen.
- The accuracy of your own Feed. Everything downstream is only as correct as what you pasted in. Check the output against the source before it leaves your hands.
There’s also a real limit on how much you should paste. Everything in your Feed section goes to a third party. On consumer accounts — free and paid alike — conversations are generally eligible to be used for model improvement unless you turn that off in settings; business and enterprise accounts exclude it contractually. That boundary is account type, not price, and it’s the single most misunderstood point in this area. Our practical privacy guide goes through the settings.
Public information, your own drafts, anonymised examples, published figures.
Client names, internal metrics, anything that identifies a specific person.
Credentials, personal data, unreleased financials, anything under NDA.
Redaction rarely costs you output quality, which is the part people don’t expect. “Our largest client in the public sector” carries almost all the signal the real name does, and the model writes the same email either way. Knowing how to give AI context safely is mostly a habit of swapping identifiers for descriptions before you paste.
Stop Retyping the Same Background
Once you know how to give AI context properly, the next annoyance arrives fast: your Frame and Fence barely change, and you are typing them again every Monday. Every major tool now has somewhere to park them permanently — custom instructions, projects, or saved assistants, depending on which tool you’re in.
One decision: is this a one-off request, or something you'll send a version of every week?
Frame belongs in saved settings almost always — your role and your audience rarely change. Fence belongs there if your house style is fixed. Feed almost never does, because the facts are what make each request different. Our walkthrough of Claude Projects covers the mechanics of the saved-workspace approach in one tool; the same logic applies wherever you work.
The settings panel where persistent background context is stored — custom instructions, project instructions, or a saved assistant's instruction field.
Crop to the instruction field and its heading. Hide any real project names, account details and confidential text.
File: claude-projects-persistent-context-setup.png · Alt: "Settings panel used to store persistent background context for an AI assistant"Google makes the same point in its Workspace prompting guide, which lists context as one of four things every effective prompt needs and recommends pointing the tool at your own documents rather than retyping their contents. Where a connected file can carry the Feed layer for you, let it.
Start with one recurring task — the report you write every Monday, the update nobody enjoys drafting. Build the block once, keep it somewhere you can find it, and reuse it next week. The second time is where the time saving actually shows up, and it compounds from there. If you want the full Think, Structure, Execute, Refine loop that this fits inside, that’s the next thing worth reading.
Build your first context block this week
- Pick one task you repeat every week and open a blank chat. Type FRAME, FENCE, FEED and TASK on separate lines before writing anything. Write your role and your reader under FRAME — two sentences, no more. Set tone, length and structure under FENCE, pairing every ban with a replacement. Paste the real dates, figures and names under FEED, then forbid guessing. Save the finished block so next week is a copy-paste, not a rewrite.