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Riley Brown describes improving an AI skill by testing its output, asking the AI to revise the skill after a failure, and repeating the test in a new conversation. His approach emphasizes outcomes and a clear standard of quality.
- 1Begin with a testAsk the agent to perform a task using the skill and evaluate what it produces.
- 2Revise after a failureGive feedback about what went wrong and ask the AI to update the skill.
- 3Clear the context and test againBrown starts a new chat and checks whether the revised skill achieves the desired outcome.
Das Wichtigste
- Test a skill on a real task against a specific standard.
- Describe failures and ask for a targeted skill revision.
- Retest in a fresh conversation to reduce the influence of prior context.
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01
An iterative skill-improvement loop
Brown recommends testing, feedback, revision and a fresh-context retest.
Riley Brown describes an outcome-based way to improve an AI skill. Ask the agent to use the skill on a task and judge the result against your standard. If it fails, explain what went wrong and ask the AI to revise the skill. Then open a new chat so the earlier conversation no longer supplies the same context, and test again. Repeat as needed: Brown's focus is on whether the workflow produces the intended outcome.
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