Improve and Review Prompts

Treat prompting as an iterative process. Review the output, identify which expectation was missing, update the controlled instruction in Agent Prompts, and test it again on representative cases. Use Feedback to inspect response-quality evidence and the Library to distribute an approved reusable starting point.

Pre-Run Quality Check

Check	Question
Objective	Is the real business goal clear?
Audience	Does the agent know who will use the output?
Context	Are essential facts, definitions, and sources supplied?
Task	Is the requested action specific?
Format	Is the expected output structure defined?
Evidence	Are acceptable sources and citation needs stated?
Unknowns	Does the prompt explain what to do when information is missing?
Restrictions	Are security, policy, tone, and action boundaries included?
Actionability	Will the result support a decision or next step?
Reusability	Could a teammate use the same template safely?

Diagnose the Output

• If it is generic, add the decision, audience, and evaluation criteria.
• If it invents facts, constrain sources and define how to report unknowns.
• If it is hard to review, define a table, fields, or section order.
• If tone varies, supply a short approved example.
• If content is incomplete, list mandatory sections and evidence.
• If it takes an unsafe action, remove the tool or require confirmation.

Maintain a Prompt Library

Keep a small set of templates for recurring work. Record the owner, purpose, expected sources, compatible agent, output contract, restrictions, test examples, and review date. Retire templates when policy or workflow changes make them unsafe or misleading.

The goal is not more prompts. It is a smaller set of tested instructions that teams can understand and improve.

Next, see how the same source, specialist-agent, and human-review pattern scales in Enterprise AI Use-Case Patterns. Agent owners can use Evolution to review prompt improvements grounded in feedback.