Writing a Prompt to Help for AI Discovery Work

A prompt to help for AI discovery has one job: describe the shape of a subject before you know enough to ask a good question about it. Most prompts are written for the opposite situation, where you know what you want and are negotiating over format.

Discovery work starts from a gap. You inherited a codebase nobody documented, or you were handed a regulatory regime you had never heard of on Monday and asked for a position by Friday.

A model helps because it has read more of the surrounding material than you will read this quarter. It hurts because it produces a confident, well organised map of territory it has only partly seen, and nothing in the output separates the surveyed parts from the sketched ones. Every prompt below is built to make that seam visible.

What prompt engineering in AI means when you are still discovering

Prompt engineering in AI is the practice of designing the text you send a model so the result holds up on every run. For discovery, holding up means something narrower: the structure should be stable across runs, and the guesses should arrive labelled as guesses.

The default failure is fluency. Ask a model what you should know about an unfamiliar compliance regime and you get twelve headings that read like a competent consultant's summary. Eleven are accurate. One is an obligation that does not exist, and it will cost you a week once a lawyer reads your memo. The prose gives no signal either way, because writing quality stays flat across a model's confidence range.

So ask for a structure with room for doubt. Anthropic's prompting guidance for research tasks names three things a discovery prompt should carry:

• A definition of a successful answer, written before the search starts.
• Several competing hypotheses held at once, so a single narrative cannot take over early.
• Confidence levels tracked in the notes as the work proceeds.

The third has research behind it. In Language Models (Mostly) Know What They Know, Kadavath and colleagues found that larger models are well calibrated on multiple choice and true-or-false questions when the format is right. That calibration degraded on unfamiliar tasks, which is precisely the condition you are in during discovery.

So a model's self-reported confidence is a weak signal which becomes usable once you constrain the format. Ask an essay how sure it is and you get the word "fairly". Ask for a number between zero and one and you get a column you can sort.

AI prompt examples that open a subject up

An opening prompt should return a map. Three patterns do most of that work, and each asks for a structure the model cannot fake without the fake becoming visible.

The boundary prompt, for finding out how big the subject is:

List the sub-areas of <topic>. For each one, give the term of art
practitioners actually use and one sentence on the decision it governs.
Flag any term that is used inconsistently across the field.

The vocabulary prompt, for reading anything serious without stopping every paragraph:

Give me the 15 terms I need in order to read a technical paper in
<field> without looking things up. Define each in one line. Mark any
that mean something different in an adjacent field.

The dissent prompt, which is usually the one that changes your plan:

What do practitioners in <field> currently disagree about? For each
disagreement, state both positions in their strongest form and name the
evidence that would settle it.

What these share is a demand for a checkable shape. You can test fifteen terms against a textbook index in ten minutes. Testing a five-paragraph summary means reading the field first, which was the thing you were trying to avoid.

Notice that none of them opens with "act as a world-class expert in". Role prompting has real uses, though at the top of a discovery prompt a role mostly decides which register the guesses get written in.

Each pattern also tells the model what the answer is for, a separate lever with its own article on why giving a prompt its reason changes the output.

Generative AI prompt examples that force competing hypotheses

The most useful move in discovery prompting is refusing the model its first answer. Models are trained to produce one coherent response, and coherence gets achieved partly by discarding the alternatives early. So ask for them out loud:

Here is what I observe: <observation>.
Give me at least four explanations that would produce it, including two
that a domain expert would consider and a newcomer would miss. For each
one, name the single observation that would rule it out.

The last line is the one that pays. An explanation with no disconfirming observation attached is either untestable or padding to reach four, and both are worth knowing.

It will sometimes hand you a fourth explanation that is the third one wearing a hat. Still information: the space is smaller than you assumed, or the reading behind it has run out.

This is where generative AI prompt examples for research diverge from the ones circulating on social media. A prompt producing an impressive answer in one shot is optimised for the screenshot. A discovery prompt is optimised for the second question you ask, which is why its output looks messier and is worth more. The same logic scales up to how AI agents change research work.

How system prompts and models of AI tools change what you find

A system prompt is the standing instruction a tool sends ahead of your message, every time, without showing it to you. It sets the hedging behaviour and the formatting defaults, which is why the same question gets different answers from two chat products running the same model.

You cannot read a consumer tool's system prompt, though you can override a good deal of it from your own message. If a tool returns tidy bulleted overviews when you want a dense technical read, say so at the top. If it hedges every claim, ask for the hedges collected into a list so you can count them.

Where the documents sit matters as much as the wording. Anthropic's long-context guidance is direct: put long documents at the top of the prompt, above your question and instructions. The docs report that queries placed at the end improved response quality by up to 30 percent in testing, most visibly on complex multidocument inputs.

The reason turns up in the research. Lost in the Middle, by Liu and colleagues, found that performance is highest when the relevant passage sits near the beginning or the end of a long input, and degrades significantly when a model has to reach for something buried in the middle. Models sold on their long context windows showed the same curve.

The consequence for discovery is blunt. Dumping 200 pages of vendor documentation into one prompt and asking what matters surfaces whatever landed in the first and last stretch. Split the corpus and ask per document, then combine the answers yourself.

Asking the model to quote the passage it relies on before answering helps too, and Anthropic recommends that for long document work. Producing the quote first makes an invented passage harder to reach, though not impossible.

How to organize AI prompts so a discovery run repeats

A discovery prompt that worked once is an asset only while you can find it again. Most people cannot, because it is three weeks back in a chat history whose search has no idea what they were doing.

An AI prompt library fixes that for about ten minutes a week, and it need not be a product. A folder of markdown files in the repository beats a paid tool nobody opens. The library living in a Slack message you sent to yourself also counts, technically, until the day you need it.

Each entry wants four fields:

• The prompt text, with the variable parts marked so a later reader sees what is meant to change.
• The model and the date it was tuned on. This is how you know when to retest.
• One saved output you judged good, so the next person has a target.
• The question it answers, in one line. Six months later this is the only field anyone searches on.

The version field is the one people skip and then regret. Prompts drift against model updates the way integration tests drift against an upstream API, quietly and then all at once. Treating the text as a maintained artefact runs through the practices that keep prompts working over time, and it is the subject AI Prompt Engineering spends its second half on.

Four moves before your next discovery prompt

None of this needs new tooling. It needs four habits, and the first carries most of the weight.

1. Ask for the map before the answer. Terms and open disagreements first, recommendations later.
2. Ask what would change the model's mind. An explanation with no disconfirming test attached is decoration.
3. Put the documents above the question, and split a large corpus instead of trusting one enormous prompt.
4. Save the run into a file you can grep, with the model and date beside the prompt.

Discovery is the phase where being wrong is cheap and staying wrong is expensive. A prompt that hands you a clean answer first time has usually skipped the part where you find out which half to check.