Basic prompt structure

"Tell me about dogs" is a grammatically complete sentence. It is also a prompt that will produce a generic answer about dogs — domestication history, breeds, care requirements — none of which is what you wanted. The problem is not the question. The problem is that the question contains no information about what useful would look like for you.

A good prompt has three parts: task, context, and output format.

Task

The task is what you want done. Not "tell me about" — that is a category of answer. The task should be a verb with a specific object: summarize, classify, rewrite, extract, compare, draft.

Wrong: "Tell me about machine learning." Right: "Explain the difference between supervised and unsupervised learning."

The right version has a specific verb (explain) and a specific object (the difference between two things). Claude now knows what kind of answer to produce.

Context

Context is the background Claude needs to produce a useful answer. Who is the audience? What constraints apply? What does the person already know? What should the answer avoid?

Without context, Claude makes assumptions. Those assumptions may not match yours. Give Claude the information it would need to write something specific rather than generic.

Wrong: "Explain machine learning." Right: "Explain the difference between supervised and unsupervised learning to a non-technical marketing manager who needs to understand why our recommendation system sometimes surfaces irrelevant products."

The right version tells Claude the audience (non-technical marketing manager), the frame (recommendation system), and the purpose (explaining a specific behavior). Claude can now write something that will actually be useful to that person.

Output format

Format is how the answer should look. Paragraph? Numbered list? JSON? A table? 50 words or 500? The format constraint is often the most neglected part of a prompt, and it is frequently the most important. A five-paragraph essay and a three-bullet summary of the same content serve completely different purposes.

Right: "Explain the difference between supervised and unsupervised learning to a non-technical marketing manager in three bullet points, each under 25 words."

Putting it together

When you write a prompt, run this quick check: Does the model know what verb to perform? Does it know who it is writing for and what constraints apply? Does it know what format to produce? If any of these is missing, the prompt will produce something that might be right, or might not, and you will not know why.

If you get stuck

If the output is still too generic after adding context, the context is likely still abstract. Replace adjectives with specifics. "Non-technical" is abstract; "a person who manages email campaigns and has never written code" is specific. "Brief" is abstract; "under 50 words" is specific.

If you are unsure what format to request, look at an example of a good answer in that domain and describe its structure back to Claude.

Cross-reference

L4 (Being clear and direct) goes deeper on specificity. L7 (Separating data from instructions) covers what to do when the context itself is a large block of text.

Deliverable

Take this prompt: "Write an email." Add task, context, and format until it produces the specific email you actually need. The prompt should be at least three sentences. The output should be something you could send.

Further reading

• "Nouns and Verbs" — why a prompt that names the noun and the verb explicitly produces a different class of output than one that gestures at either.