To get a more useful answer from an AI assistant, state the task clearly, provide the context it needs, and specify what the response should look like. Then review the result and refine your request. These 20 approaches are practical starting points—not a secret formula or a guarantee of accuracy. Models and versions can behave differently, so test advice on the system you use.
Start with a clear task and enough context
A prompt is a set of instructions for a model. OpenAI describes prompt engineering as “the process of writing effective instructions for a model, such that it consistently generates content that meets your requirements” in its API documentation. In everyday use, the same principle is simple: say what you need, give the model relevant information, and make the desired result observable.
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1. Lead with the job
Use a direct verb: summarize, compare, draft, explain, extract, or revise. “Give me information about electric cars” leaves the task open; “Compare these two electric cars for a first-time buyer” gives the assistant a job to do.
2. Add the reason or goal
Explain what you will use the answer for when that changes what matters. A summary for a meeting may need decisions and action items; one for a student may need key concepts and definitions. Anthropic’s prompting best practices recommend including useful context or motivation behind instructions.
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3. Supply the missing background
Include facts, prior decisions, definitions, or source material that the model cannot reliably infer. OpenAI’s API guidance and Microsoft’s Copilot prompting guide both emphasize providing relevant context.
4. Name the intended reader
Say whether the answer is for a beginner, a specialist, a customer, or another audience. That helps set the vocabulary and depth. For example: “Explain this error message to a nontechnical office worker.”
5. Set tone and style when they matter
Ask for a friendly, formal, concise, or otherwise appropriate voice if it affects the task. Tone is useful direction, but it does not replace a clear task or relevant facts. OpenAI’s prompting best practices call out tone and style as elements to specify.
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Specify what a useful answer should contain
6. Define the output shape
Request a format you can use: a table, numbered steps, a short explanation, or a draft email. Anthropic’s guidance recommends being explicit about output format and constraints.
7. Set scope and boundaries
Say what to focus on and what to leave out. “Compare battery life and repairability; skip design” is more actionable than “keep it relevant.” For a broad task, identify the highest-priority areas rather than asking the model to cover everything equally.
8. Give a useful length or level of detail
Ask for a quick overview, a specified word count, or a detailed explanation when length matters. Microsoft’s Copilot guide illustrates how audience and a 1,500-word target can make a request more specific. Treat a word count as guidance, not a substitute for saying what the answer must cover.
9. Identify the information source
If you want the model to use a particular file, email, or document, name it explicitly and say what to do with it: “Summarize the attached policy and list the deadlines.” Microsoft’s guide recommends specifying relevant information sources.
10. Separate the task from the source material
Label the instruction and the material separately so the model can distinguish what to do from what to process. For example, write “Task: extract the dates” and “Source text:” before pasting the content. Clear structure and examples are endorsed in both Anthropic’s prompting guidance and Microsoft’s Copilot guide.
11. Order instructions deliberately
Put the most important task and constraints where they are easy to identify. The best order can depend on the model and task; Microsoft notes that instruction order can affect Copilot responses and recommends experimenting with it in its prompting guide.
12. Use positive, actionable directions
Describe what you want the assistant to do, not only what you do not want. “Use plain language and define technical terms” gives a clearer target than “don’t be confusing.” When the response depends on a condition, make it explicit: “If the document does not state a deadline, say that none is given.” Microsoft’s guide recommends positive instructions and suggests if-then instructions.
Make complex or repeatable work manageable
13. Break large requests into focused steps
Separate a multi-part task into smaller requests when that makes the work easier to direct and inspect. For example, first ask for a summary of a long report, then ask for a comparison of its recommendations. OpenAI recommends prioritizing focus areas for complex requests in its prompting best practices.
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A short sample can show the format, style, or classification you mean more clearly than a description alone. Keep examples relevant to the task; they are demonstrations, not proof that the same answer fits every case. OpenAI’s API guide and Anthropic’s guidance describe examples as a prompting technique.
15. Ask for alternatives when choice is useful
If you are choosing a direction, request a few distinct options and ask the model to explain their trade-offs. This is more useful than asking for alternatives when there is only one correct answer or a fixed procedure to follow.
16. Tell the model how to handle uncertainty
For tasks where guesses could mislead, ask the assistant to flag missing information or unsupported claims instead of filling gaps. That instruction can help communicate your expectations, but it cannot guarantee that the model will recognize every error. Microsoft advises users to review generated content because it can be incorrect, biased, offensive, or harmful in its Copilot guide.
17. Make success observable
State what a satisfactory answer must include so you can check it. “Include the three options, the main trade-off for each, and a recommendation tied to my budget” gives you a concrete review checklist. For repeatable applications, OpenAI’s API guidance recommends using tests and evaluation suites to measure prompt behavior.
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18. Check the first answer against the goal
Look for omitted requirements, unsupported statements, and formatting problems. Verify important facts independently, especially when the answer could affect a consequential decision. Microsoft explicitly recommends reviewing and validating generated responses in its Copilot guidance.
19. Refine one meaningful part at a time
Use the first response to identify what went wrong, then change the relevant part of the request: add missing context, clarify a constraint, simplify the task, or adjust the format. Changing one significant element at a time makes it easier to see what helped. OpenAI describes iterative refinement in its best practices and guide to creating a good prompt.
20. Re-test after model or prompt changes
A prompt that works for one model or version may not behave the same way on another. Recheck the results when the model, prompt, or workflow changes. OpenAI’s API guide, Anthropic’s current-model guidance, and Microsoft’s Foundry techniques page all caution that behavior and useful techniques can vary. Microsoft specifically notes that some techniques are not recommended for reasoning models.
A reusable prompt pattern
Adapt this pattern to the task instead of treating every field as mandatory:
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Help me [specific task] for [audience or purpose]. Use [relevant context or named source]. Focus on [priorities] and respect [constraints]. Return the answer as [format and length] in a [tone] style. If key information is missing, identify it. Check the result against [success criteria].
Remove details that do not matter, then check the output. Specific instructions and a template can make expectations clearer, but neither guarantees a correct answer. For consequential work, use relevant source material where appropriate and verify important claims rather than relying on wording alone.
Why a good prompt is not a guarantee
Prompting advice is a starting point, not a universal recipe. OpenAI says techniques can vary across model types and snapshots; Anthropic advises checking general techniques against evaluations; and Microsoft’s Foundry guidance warns that model behavior varies. A polished prompt cannot make an unreliable claim true, and an instruction to disclose uncertainty cannot ensure every gap will be caught. For workflows used repeatedly, evaluate outputs on the model and task you actually intend to use.
Vendor guidance changes over time. Microsoft’s Copilot page says it was last updated in February 2026, and OpenAI’s good-prompt page says it was updated two months before the guidance was checked on October 4, 2026. Consult the linked documentation for current, product-specific advice.
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