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Better AI answers usually come from better instructions, not from a magic phrase. State the task plainly, provide the context the assistant cannot infer, define what matters most, specify the output you want, and ask it to flag uncertainty. For difficult work, split separate jobs into stages, inspect the first answer, and refine the next prompt.
This guide gives you a reusable prompt structure, examples for common tasks, a method for handling factual work, and a way to test prompts when you change assistants or model versions.
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What makes a prompt effective
An effective prompt removes avoidable decisions. It tells the assistant what to do, why the work matters, which information to use, what constraints apply, and how the result should look. OpenAI’s Help Center summarizes the foundation this way: “Ensure your prompts are clear, specific, and provide enough context for the model to understand what you are asking.”
Clarity does not mean writing a long instruction every time. A short request can work when the task is familiar and the context is obvious. A longer prompt is justified when the assistant must satisfy several conditions, use supplied material, address a particular audience, or avoid a costly mistake.
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The five decisions a prompt should cover
| Part | Question to answer | Useful details |
|---|---|---|
| Task | What should the assistant do? | Summarize, compare, classify, draft, transform, calculate, plan, or critique. |
| Context | What does it need to know? | Audience, purpose, source text, definitions, dates, geography, constraints, and relevant background. |
| Requirements | What matters most, and what must be avoided? | Priorities, required facts, exclusions, assumptions, length limits, risk tolerance, and decision criteria. |
| Output | What should the answer look like? | Format, headings, table columns, number of items, tone, reading level, and whether to include explanations. |
| Check | How should uncertainty or errors be handled? | Ask for missing information, mark unsupported claims, preserve exact names and dates, or separate facts from suggestions. |
These parts are a planning aid, not a mandatory formula. Omit a section when it has no bearing on the task; include it when leaving the decision implicit could change the result.
Build a prompt step by step
1. Write the task as an observable action
Start with a verb and a deliverable: summarize, rewrite, extract, rank, draft, or explain. Avoid requests such as “make this better” unless you define what better means.
Compare these two openings:
- Vague: “Improve this report.”
- Observable: “Rewrite this report for a nontechnical leadership audience. Keep every number and date unchanged, remove repetition, and present the recommendations in a three-row table.”
The second version gives the assistant a testable destination. You can inspect whether it preserved the figures, changed the audience level, and used the requested table.
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Context is not a biography of the project. Include information the assistant would otherwise have to guess: who will read the result, what decision it supports, the source material it may rely on, and terms that have a special meaning in your work.
For a rewrite, identify the original audience and the new audience. For a summary, state whether the reader needs decisions, risks, deadlines, or background. For a classification task, define the categories and provide borderline examples. If the assistant must work from a document, paste the relevant text or attach it through the product’s supported workflow instead of assuming it can see material that you have not supplied.
3. Turn preferences into priorities
When instructions conflict, the assistant needs an order of importance. Say what wins: factual accuracy over brevity, preserving legal wording over stylistic polish, or a complete list over a fast answer.
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Use explicit constraints such as:
- “Keep names, units, and dates exactly as written.”
- “If the notes do not establish an answer, write ‘not stated’ rather than infer one.”
- “Prioritize safety considerations before convenience.”
- “Use plain language, but do not remove technical terms that are necessary for accuracy.”
Negative instructions are most useful when paired with a positive alternative. Instead of only saying “do not ramble,” specify “use five bullets, each no longer than two sentences.”
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Describe the shape of a usable answer. State the format, approximate length, sections, and tone. If you need machine-readable output, name the fields and require valid syntax. If you need a human-facing document, identify the heading hierarchy, list style, or table columns.
“Give me advice” leaves many choices open. “Return a two-column table with Issue and Recommended action, followed by a 100-word explanation” is an output contract that can be checked without guessing your preferences.
5. Add a checking instruction
A checking instruction tells the assistant how to behave when the request is underspecified or the evidence is weak. Ask it to list assumptions, mark uncertainty, identify contradictions, or request the one missing fact that blocks a reliable answer.
For factual work, request a distinction between information found in the supplied material and the assistant’s suggestions. A checking instruction does not guarantee correctness; it makes uncertainty visible so you can review it.
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Copy this structure and remove any line that does not apply:
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Task: [What should the assistant produce or do?]Context: [Audience, purpose, source material, definitions, dates, and constraints.]Requirements: [Priorities, required elements, exclusions, and decisions that matter most.]Output: [Format, sections, length, tone, and level of detail.]Check: [What to preserve, verify, flag as uncertain, or ask about before proceeding.]Here is the same template filled in for a meeting summary:
Task: Summarize the attached meeting notes for the product team.Context: The team needs a record of decisions made in the meeting.Requirements: Focus on decisions, owners, and deadlines. Keep names and dates exact. Do not add actions that the notes do not assign.Output: Use five bullets followed by a short “Open questions” section.Check: Mark any owner, date, or decision that the notes leave unclear.This is an illustrative template, not a guaranteed recipe. The quality of the result still depends on the source material and the assistant you use.
Choose between one prompt and several
A single prompt is convenient when the work is cohesive and the intermediate decisions do not need separate review. Multiple focused prompts are safer when the subtasks have different goals, require different context, or can be checked independently.
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| Situation | One prompt is usually reasonable | Separate prompts are usually easier to control |
|---|---|---|
| Task structure | The request is one transformation, such as turning notes into a specified summary. | The work combines research, analysis, drafting, and editing. |
| Context | Every step uses the same short source and audience. | Each stage needs different documents, definitions, or decision criteria. |
| Review | The output can be checked as one clear deliverable. | You need to approve an outline, extracted facts, or calculations before drafting. |
| Trade-off | Less setup and less copying between turns. | More control and easier diagnosis, at the cost of extra turns and handling. |
How to split a complex request
- Extract: Ask for the facts, decisions, or requirements in a neutral structure.
- Plan: Ask for an outline or options using the extracted material and stated priorities.
- Draft: Produce the requested document from the approved plan.
- Check: Compare the draft against a checklist for omissions, unsupported claims, and format errors.
Keep the hand-off between stages explicit. Paste or attach the approved output when the next stage needs it, and state which parts are authoritative. This prevents a later step from treating an early suggestion as a confirmed fact.
Use iteration instead of starting over
The first answer is often a diagnostic draft. OpenAI’s Help Center describes prompting as a conversation: “Treat prompting as a conversation: refine your requests based on initial answers and keep experimenting.” Review the answer against your goal, identify the exact mismatch, and change the prompt that addresses that mismatch.
When the answer is too broad
Narrow the scope and name the audience or decision. Replace “Explain cloud security” with “Explain identity and access controls to a finance manager choosing a small-business service. Cover account recovery, administrator roles, and audit logs in 600 words.”
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When important information is missing
Add the relevant source text, definitions, or project constraints. Tell the assistant which details are mandatory and what to do when they are absent. “Use only the figures in the supplied budget; if a category is missing, identify the gap instead of estimating” is more useful than “be accurate.”
When the structure is wrong
Name the exact form: a table with specified columns, a numbered procedure, a short email, or a set of headings. Include a count when the number of items matters.
When several jobs are mixed together
Set priorities or separate the jobs into stages. A prompt asking for research, a persuasive article, a risk assessment, and copy editing at once makes it harder to tell which instruction controls a conflict.
When the tone is wrong
Describe the reader and the desired effect rather than relying on a single adjective. “Calm and direct, suitable for a customer who has already experienced a service outage” gives more usable direction than “professional.”
Ground factual answers in reliable material
For current, specialized, or consequential questions, provide source material or use a search capability when the assistant supports one. Microsoft describes search as a way to obtain up-to-date information and help mitigate fabricated answers. Search does not remove the need for review: sources can conflict, be outdated, or fail to answer the precise question.
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A fact-sensitive prompt pattern
Task: Prepare a briefing on [topic] for [audience].Context: Use the supplied documents and, where available, current search results. The decision date is [date], and the relevant region is [region].Requirements: Separate confirmed facts, reasonable interpretations, and open questions. Preserve source dates and units. Do not fill gaps with estimates.Output: Start with a five-bullet executive summary, then provide a table of claim, supporting source, date, and confidence.Check: Flag contradictions and information that should be verified by a person before use.Attach or paste the evidence the assistant is expected to use. If you need citations, ask for them in a defined format and inspect that each citation actually supports the claim. For high-stakes decisions, verify important claims independently rather than treating a confident sentence as proof.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Account for model and version differences
There is no universal prompt formula. OpenAI notes that prompting can differ between reasoning models and other model types, and that model behavior is non-deterministic. Anthropic’s documentation likewise organizes advice by model and cautions that named techniques should be checked against evaluations before being applied elsewhere.
Consequently, a prompt that works well in one assistant, model, or version may need different wording, examples, or decomposition in another. Treat a technique as a hypothesis to test, not a guarantee.
| What to compare | How to test it |
|---|---|
| Model-specific guidance | Read the current documentation for the model or assistant you will actually use. |
| Representative quality | Run the same prompt on examples that resemble your real workload, including difficult cases. |
| Consistency | Repeat runs when variation matters and record meaningful differences. |
| Evaluation cost | Balance answer quality against the time, token use, delay, or human review required by the approach. |
Do not transfer a technique merely because it has a memorable name. Keep it only if it improves your own evaluation set.
Production and API practices
If prompts support a recurring workflow, treat them as changeable software components. OpenAI’s API guidance recommends pinning model snapshots and building tests and evaluation suites so you can detect behavior changes when prompts or model versions change.
Keep a small evaluation set
Collect representative inputs: ordinary cases, edge cases, ambiguous requests, and examples where an error would matter. Define what a passing answer must contain and what it must never do. Re-run the set after changing the prompt, model snapshot, input format, or parsing logic.
Version the prompt and its context
Record the prompt text, model or version, relevant system settings, input documents, and evaluation result. Without that record, a later improvement or regression is difficult to explain.
Test failure handling
Include cases with missing fields, conflicting documents, unusual names, and requests outside scope. Check that the assistant follows your instruction to ask, abstain, or flag uncertainty instead of silently inventing a completion.
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| Symptom | Likely cause | Prompt change |
|---|---|---|
| The answer is generic. | The task, audience, or decision is unspecified. | Name the reader, purpose, scope, and a concrete deliverable. |
| Key facts are omitted. | The source or required elements were not supplied. | Add the relevant text and list mandatory facts or fields. |
| The assistant invents details. | Gaps were left open and no uncertainty rule was given. | Require it to mark missing information and avoid unsupported estimates. |
| The format is unusable. | The output contract was vague. | Specify headings, table columns, item count, length, and tone. |
| Several requirements conflict. | No priority order was stated. | Rank accuracy, completeness, brevity, style, and other goals. |
| The response changes between runs. | Model behavior is non-deterministic or the task is underspecified. | Clarify acceptance criteria, test representative cases, and evaluate variation. |
| A long prompt produces a confused result. | Unrelated subtasks share one instruction block. | Split extraction, planning, drafting, and checking into stages. |
| A named technique works inconsistently. | The technique may depend on the model or version. | Check model-specific guidance and retain the technique only if evaluations support it. |
Worked prompts for common jobs
Summarizing supplied notes
Summarize the attached incident notes for the operations team.Focus on timeline, customer impact, root-cause evidence, owners, and next deadlines.Keep names, times, and numbers exact. Distinguish confirmed findings from hypotheses.Output: a timeline table followed by five action bullets. If the notes do not identify an owner or deadline, write “not stated.”Turning a technical explanation into customer guidance
Rewrite the supplied troubleshooting article for customers with no technical background.Preserve every safety warning and prerequisite. Replace jargon with plain language, but keep menu labels and error messages exact.Output: a short explanation, numbered steps, and a “When to contact support” section. Do not promise a result that the source does not establish.Comparing options without hiding trade-offs
Compare the three options in the supplied documents for a small team choosing a six-month solution.Use the criteria cost, setup effort, required skills, limitations, and reversibility. Treat the supplied figures as authoritative and preserve their dates and units.Output: a comparison table, then one paragraph explaining which option fits each of three stated priorities. Identify any criterion for which the documents provide no value.Drafting an email from a decision
Draft an email to the project team announcing the decision recorded in the attached notes.State what was decided, why, who owns the next actions, and the dates. Do not add rationale that is absent from the notes.Use a calm, direct tone and keep the body under 250 words. End with a short list of open questions, if any remain.A practical review checklist
- Can a reader identify the requested action in the first sentence?
- Did you provide the audience, purpose, scope, and source material that affect the answer?
- Did you state which goals take priority when they conflict?
- Is the required output format explicit enough to inspect?
- Did you say how missing, conflicting, or uncertain information should be handled?
- Would separate stages make the work easier to review?
- For current facts, did you supply reliable material or request search and verification?
- If the prompt will be reused, do you have representative tests and a way to record changes?
The operating rule
Write the smallest prompt that removes the important ambiguity. Give the assistant the context it cannot infer, define a checkable output, and make uncertainty visible. Then review the answer and refine the instruction instead of expecting one wording to work unchanged across every task, model, and version.
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