Test and refine prompts systematically
Use examples, rubrics, and controlled changes to improve prompt reliability.
- Create a repeatable prompt evaluation and iteration loop.
Prompt engineering is an empirical process. Build a representative set of inputs, define what a successful output means, run the prompt, inspect failures, and change one thing at a time. Compare versions on the same cases so you can tell whether a change helped. Include ordinary inputs, edge cases, ambiguous requests, and cases where the model should decline or ask for clarification.
A small example
cases = ["clear request", "ambiguous request", "missing information"]
for case in cases:
print(f"Evaluate prompt with: {case}")Evaluate prompt with: clear request Evaluate prompt with: ambiguous request Evaluate prompt with: missing information
A rubric can score correctness, completeness, format, and appropriate uncertainty. Use human review for judgments that automated checks cannot capture. Prompts can behave differently across models or model updates, so rerun evaluations when the deployment changes.
Key takeaways
Create a repeatable prompt evaluation and iteration loop.
Test prompts on realistic inputs and validate important outputs in software.
Lesson quiz
5 questions · pass with 4 correct · up to 50 XP
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