The idea
A prompt is the instruction you give an AI model. For a developer, it might ask a chat assistant to explain a function, investigate an error, or suggest a small refactor. A short request can work. But when the task is underspecified, the tool has to guess important details: who the answer is for, which code matters, and what a useful answer looks like.
A reliable starting structure has three parts:
- Goal: Say what you want done.
- Relevant context: Provide the information needed to do it.
- Expected result: Specify the output format, level of detail, and any limits.
This is not a magic syntax. It is a way to make the decisions in your head visible in the request. OpenAI recommends clear instructions and relevant context. Anthropic recommends direct instructions and explicit output formats. GitHub likewise advises developers to be specific about requirements and to provide examples where useful. OpenAI · Anthropic · GitHub
Plain English: Tell the tool what job it has, give it the information needed for that job, and describe what a finished answer should contain.
The goal is usually a verb plus an object: “explain this function,” “identify likely causes,” or “propose a refactor.” Context is not everything you know about the project. It is the code, error message, runtime detail, expected behavior, or audience that changes the answer. The expected result makes a response easier to scan and evaluate: ask for bullets, a table, affected files, tests, or a maximum length.
You can add a constraint when it matters. A constraint is a boundary, such as “do not change the public API” or “use no more than five bullets.” For a task where order or coverage matters, request numbered steps or a checklist. Treat the first version of a prompt as a draft: inspect the answer, then adjust the request when it misses an important detail.
A small example
Example: You want an explanation of a supplied Python function. A vague request is: “Explain this function.” It does not say whether you need a beginner explanation, a review of correctness, or help using the function.
A clearer prompt could be:
Explain the following Python function for a developer with six months of Python experience. State its purpose, inputs, and return value. List two possible edge cases. Use at most five bullets and include one example call. Do not change the code. Code:
[paste the function]
The goal is to explain. The relevant context is the function and the intended audience. The expected result is the requested content, bullet limit, and example call. “Do not change the code” is a constraint.
This structure does not guarantee that every statement is true. It does make omissions easier to spot. If the answer lacks an input description or provides six bullets, you can identify the missed requirement quickly. If you need a different response, change one part deliberately: add the error message, ask for a different audience, or require tests alongside a refactor proposal.
Try it
Spend five minutes on one real request you recently gave an AI tool. Do not choose a sensitive codebase; use a small, safe snippet or a fictional task.
First, write the goal in one sentence. Next, add only the context that changes the answer. Finally, define a result you can check, such as “three likely causes, one diagnostic step for each, and no code changes.” Run the original and revised requests if your tool permits it. Compare whether the revised answer is easier to review. This is an exercise, not a benchmark: one comparison cannot prove that a structure works best for every model or task.
Watch out
More context is not automatically better. Unrelated files and long, unstructured material can obscure the actual task. Keep the request focused, then add information when the first answer reveals what is missing.
A clear prompt is also not a correctness guarantee. AI-generated explanations and code can be wrong, incomplete, or unsafe. Validation means checking a result against evidence, tests, and relevant development tools. GitHub advises developers to understand and review generated code and to validate it with tests and tooling. GitHub
Finally, avoid treating one provider’s formatting suggestion as a universal rule. Examples, role instructions, and markup can be useful techniques, but they are optional. Start with goal, relevant context, and expected result. Add structure only when it helps you inspect the answer.