Good prompt writing starts with a clear task, the context the model needs, and any requirements for the answer’s audience, tone, or format. Review the first response and refine the prompt where it misses the mark. A prompt is the input or instruction that guides a model’s response; prompt engineering is the practice of designing and improving that input to get a result closer to your goal.
What makes a prompt clear?
Tell the model what to do rather than relying on it to infer your goal. Add only the background that affects the answer, then state any requirements that matter: who the answer is for, how it should sound, how long it should be, or what shape it should take. OpenAI’s API guidance recommends clear, specific instructions and separating instructions from context.
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For example, “Explain this” leaves the subject, reader, and desired level of detail unclear. A more useful request might be: “Explain the following paragraph about password managers to a nontechnical adult in three short bullet points. Use plain language and do not add facts that are not in the paragraph.”
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesA reusable prompt pattern
Use the parts that solve a real ambiguity; a simple request may need only a task and context. This is a practical pattern, not a required format from any one provider:
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Task: [the action you want the model to take]
Context: [relevant background or source material]
Audience and tone: [who will use the answer and how it should sound]
Output: [format, length, or structure]
Constraints: [important inclusions, exclusions, or boundaries]For instance, when asking for a summary, name the source text, intended reader, and format. If the answer must stick to supplied material, say so. Constraints are most useful when they prevent a likely misunderstanding; adding arbitrary requirements can make a prompt harder to follow.
How to include source material
Keep material the model should work on visibly distinct from your instructions. A heading, quotation marks, or another clear boundary can show where the source begins and ends. Then specify what to do with it—for example, summarize it, extract its main claims, or explain a particular section. This is especially helpful when a prompt contains both instructions and a long passage.
When the output needs a particular structure or style, a short example can make the target easier to understand than a broad description alone. Anthropic’s Claude prompting guidance recommends well-crafted examples for improving accuracy and consistency with its models. That is model-specific guidance, not a guarantee that examples will improve every system’s response.
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How to improve a prompt after the first answer
Treat the first response as a draft to assess, not proof that the prompt was complete. OpenAI’s ChatGPT guidance recommends reviewing the result and refining the request. Make the next prompt address the mismatch you actually saw:
- The answer is too broad: add relevant background, the intended audience, or the specific question to answer.
- The format is wrong: name the structure, such as a table, numbered steps, or a short paragraph.
- The model missed the source’s meaning: separate the source from your instructions and point to the passage or detail that matters.
- The tone or level is off: describe the reader and the language you want, or provide a brief example.
Change the prompt based on the observed problem, then check whether the new response meets the original goal. This keeps refinement focused instead of adding instructions indiscriminately.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When to keep it simple—and when to add detail
A one-off question may work well with a direct task and one sentence of context. A repeatable task, a response with strict formatting needs, or a request involving substantial source material usually benefits from more structure. Use examples when the desired output is difficult to describe precisely. The aim is not to write the longest possible prompt; it is to provide enough information to remove important ambiguity.
For everyday chat, clear requests and follow-up refinement are a sensible starting point. API prompts and other structured workflows may need more deliberate separation of instructions and data. OpenAI’s prompt engineering guide and Anthropic’s Claude guidance discuss techniques for their respective model contexts; advice for one system should not be treated as a universal guarantee.
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