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Good prompts make the task, relevant context, and desired result clear. There is no secret formula: write a specific request, check what the model returns, then revise based on what did or did not work. This practical workflow applies to ChatGPT and other large language models, though the best approach can vary by task and model.
What is prompt engineering?
Prompt engineering is the process of designing and optimizing the instructions and information given to an AI model so it is more likely to produce a useful response. It is best treated as a repeatable process—not a trick that guarantees correctness. OpenAI describes it as designing and optimizing inputs to guide a model, and its guidance, along with Google’s, emphasizes iteration.
For everyday use, that means moving from a vague request such as “Write about budgeting” to a request that identifies the job, audience, context, and form of the answer.
How to write a good prompt
- State the task and purpose. Say what the model should do and why. For example: “Explain how a household can start a monthly budget to someone who has never tracked expenses.”
- Give relevant context. Include background information, source material, or constraints that affect the answer. Context can give the model information it would not otherwise have and can limit the response to selected material. Avoid adding background that does not help with the task.
- Describe what a successful answer looks like. Specify useful requirements such as scope, tone, length, format, or points to exclude. If missing information matters, say how the model should handle it—for example, ask a clarifying question or identify what is unknown.
- Request an observable output. Ask for a particular structure, such as a checklist, table, or short explanation, when that structure will make the result easier to use. For complex JSON requirements, use a structured-output feature if the model or API provides one; prose instructions alone may not reliably enforce a complex schema.
- Add examples when they clarify a pattern. A representative input-and-output example can demonstrate a format, tone, or transformation more clearly than abstract instructions. Keep examples consistent with the request. More examples are not automatically better: Google warns that too many can cause a model to overfit to them.
- Test and revise. Try the prompt on realistic inputs, including likely edge cases. Compare the responses with your stated requirements, then revise based on recurring failures rather than personal preference alone.
A reusable prompt pattern
Use this as a starting point, not a guarantee of accuracy:
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Do [task] for [audience and purpose]. Use [relevant context or source]. Return [format]. Follow [constraints]. If [information is missing or uncertain], [say how to handle it].
For example: “Summarize the following meeting notes for a project manager. Use only the notes below. Return decisions, action items, and unresolved questions as three bullet lists. Do not infer owners or deadlines; mark them as unspecified if the notes do not say.”
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How to improve a prompt when the answer misses the mark
First identify the failure: did the model misunderstand the task, ignore relevant context, violate a format requirement, or make an unsupported assumption? Then choose a change that addresses that specific problem. If practical, change one meaningful element at a time so you can tell whether the revision helped.
- The answer is too broad: narrow the scope, identify the intended reader, or name the material the model should use.
- It misses a requirement: state the requirement directly and make it checkable, such as “include exactly three options” or “return valid JSON matching this schema.”
- It invents details: provide authoritative source text and instruct the model not to fill gaps with guesses. Ask it to label uncertainty or missing facts.
- The format varies: provide a clear example or use a supported structured-output feature for machine-readable data.
- It fails on some inputs: test a varied set, including edge cases, and look for a common failure pattern before adding more instructions.
When repeated prompt edits do not fix the problem, more prompt text may not be the answer. The issue may be missing or poor-quality context, unavailable tools, a need for retrieval, a task that should be split into smaller steps, or a model that is not a good fit. OpenAI also warns that long prompts can lead to information in the middle being missed; test with the context sizes your real task requires.
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How to test whether a prompt works
Define success before optimizing. For a summarization prompt, success might mean that key decisions are included, unsupported claims are absent, and the output follows a required format. For a classification task, success might mean correct labels across representative examples, including ambiguous cases.
- Write down the criteria that matter to the user or application.
- Prepare a small set of realistic examples, including difficult or unusual inputs.
- Run the same inputs through the prompt and record whether each criterion is met.
- Revise one important element where possible, then compare results against the same examples.
- Repeat with additional varied inputs before relying on the prompt in a recurring workflow.
Anthropic recommends defining success criteria and preparing empirical tests; OpenAI likewise recommends systematic evaluation. A prompt that works on one example may still fail on another, so judge changes against a set rather than a single appealing response.
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Do prompting techniques work the same across AI models?
No single prompt style is guaranteed to transfer unchanged. Model families and even snapshots of a model can respond differently. OpenAI, Anthropic, and Google each publish guidance for their own systems, and Google’s Gemini recommendations should not be treated as universal rules for every model.
For example, examples, explicit constraints, or a particular output format may help one task while adding needless complexity to another. Compare approaches on the same realistic inputs and criteria. When changing a model or version, recheck the prompt instead of assuming the old behavior will persist. Provider documentation is a useful starting point:
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- OpenAI’s ChatGPT prompt engineering best practices cover clarity, specificity, context, and iterative refinement for general users.
- OpenAI’s API prompt engineering guide discusses model and snapshot sensitivity, clear instructions, and evaluation.
- Anthropic’s prompt engineering overview explains success criteria and how to decide whether prompt engineering can address a failure.
- Google’s Gemini prompt design strategies cover examples, constraints, response formats, and iteration. The page was last updated September 17, 2026.
Does asking a model to reason step by step help?
It can help in some settings, but evidence from a particular benchmark is not a promise of improvement on every task or current model. In a 2022 study, Jason Wei and coauthors reported that eight chain-of-thought exemplars with PaLM 540B achieved then-state-of-the-art accuracy on GSM8K, a math benchmark. The study also found that gains were very small or negative on the easiest single-operation subset. Those results concern that historical model, benchmark, and experimental setup—not a universal rule for today’s assistants.
For practical work, evaluate whether a reasoning-oriented instruction improves the final answer against your success criteria. Do not assume that a longer prompt or a request for visible reasoning will solve a problem caused by missing facts, unsuitable tools, or a poor model fit. See the 2022 Chain-of-Thought Prompting study and OpenAI’s guide to optimizing LLM accuracy for the underlying research and broader accuracy considerations.
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