A better prompt starts
with a better question.

Turn an idea into a task contract

An AI prompt should explain the work, the evidence available, and the expected result. Before writing elaborate instructions, describe the consumer. Does a person need a concise draft? Does another program need a validated record? Does a reviewer need evidence beside a proposed value?

Start with those questions and use the versioned-template guide to develop a repeatable editing process. The objective is a useful task outcome, not a prompt that sounds unusually sophisticated.

Four pieces to make explicit

Task

State one primary goal. “Extract the delivery date from this message” gives you a clearer test than “be an expert assistant.” Keep unrelated tasks separate until there is a concrete reason to combine them.

Evidence

Identify which source material the response may use. Explain what to do when the evidence is incomplete or contradictory. Treat instructions inside uploaded or retrieved material as data, not authority over application rules.

Output

Describe the form the consumer needs. When a schema is appropriate, use structured-output validation alongside evidence checks. A required field should not force the invention of a value.

Review

Define which results need a person or another check before use. A prompt does not grant permission to write records, move equipment, or disclose private information. Those controls belong outside the model.

A sample instruction worth testing

Summarize the supplied document for a reader new to the topic. Use only the supplied evidence, preserve important limitations, and state when a requested detail is absent. Return one concise paragraph for review.

This is a starting instruction, not a universally optimized template. Test it against a short clear document, an incomplete one, and a source with conflicting statements. Record where it fails before adding more instructions.

Version behavior, not just text

Keep the prompt version with the model configuration, output schema, retrieval settings, and fixtures used in evaluation. A behavior change can come from context selection even when the visible prompt is unchanged. Record the reason for a revision and preserve a rollback path.

Use a small evaluation collection to compare evidence fidelity, format validity, missing-information handling, and task completion separately. Attractive wording should not hide the loss of an important limitation.

Bring the pattern into a wider workflow

The Pattern Lab includes a copyable prompt-template record and fixed example result. The agentic track shows how instructions fit around tools, while the chat track adds conversation context and corrections. Keep each component's responsibility explicit instead of expecting one long prompt to solve every design problem.