GPT Prompt Engineering: Turn Good Chat into an Evaluable Workflow

Five reusable prompt modules

A reliable prompt usually states the task, input material, constraints, output format, and acceptance criteria. Without one of these, the model has to guess and results drift.

1. Specify the task, not just the tone

“Make it professional” is hard to evaluate. “Extract product, quantity, destination port, and lead time from this buyer email; return null for missing fields” is testable. Turn vague requests into actions and fields.

2. Supply references and boundaries

State which facts must come from the supplied material and which gaps must be marked for confirmation. This matters for quotes, certifications, and logistics.

3. Use examples to calibrate format

One to three strong input/output examples usually beat a page of adjectives. Include normal and edge cases: multiple products, mixed languages, and missing information.

4. Define the failure path

Say “do not guess,” “ask for the missing field,” and “route sensitive cases to a human.” That turns risk into an operating procedure.

5. Iterate against an eval set

Keep 20 to 50 anonymized real examples. After each prompt change, compare field accuracy, correct abstention, edit rate, and cost. Ship the version that improves measurable outcomes.

Official source: OpenAI Prompt Engineering Guide. This is an original methodology companion, not a reproduction of the official examples.