Turn a Request Into a Testable Contract
A strong prompt does not merely describe what to write. It makes success observable before generation begins. Translate an ambiguous request into an outcome, evidence standard, constraints, and acceptance checks. Decompose complex work into stages that can be inspected and corrected independently. Choose between direct prompting, examples, structured sections, iteration, and workflow redesign. Diagnose prompt failures without treating every bad output as a model failure. Build reusable prompt packets for high-value Claude workflows. An operations manager asks Claude to "research our customer complaints and create a persuasive executive report with recommendations." The result is fluent. It includes four recommendations, two trends, and a clean table. It is also unusable. One recommendation conflicts with policy. The table combines two date ranges. A regional exception is missing. Nobody can tell which complaint supports which claim. The team tries three repairs. They add "be accurate." They ask Claude to "think harder." Then they paste the same request into a more capable model. The prose improves, but the evidence problem remains. The request never defined the decision, the permitted sources, the required coverage, the audience, or the test for a supported recommendation. Claude optimized for a plausible report because plausibility was the only visible target. A prompt is an interface between human intent and model behavior. Good interfaces expose inputs, constraints, outputs, and failure…
Turn a Request Into a Testable Contract: A strong prompt does not merely describe what to write. It makes success observable before generation begins.
This free lesson is part of the AI Engineering from Scratch curriculum. Read the full explanation, run the lesson code, and verify the result in the interactive reader or from the repository source.
Browse the complete course catalog or open this lesson on GitHub.