Intro:
Writing test cases for conversational agents can feel like a chore. If your agent includes an FAQ component, chances are you’ll spend hours manually listing questions and expected answers before you even start evaluating. But what if you could generate a solid starting point in minutes?
Here’s a quick hack that uses the agent’s own knowledge base to auto-generate test cases, run an initial evaluation, and create a review-ready draft for your process owner.
Why This Hack Matters:
- Manual effort is high: Creating 20–30 test cases from scratch is time-consuming.
- Most agents have FAQs: Perfect for leveraging their existing knowledge source.
- Evaluation needs structure: A quick baseline helps you iterate faster.
Copy this table into a spreadsheet. These are your initial test case questions.
Now using the evaluate feature in copilot studio, upload this test set and allow the LLM to capture the agent response
The output will include columns like:
- question
- actualResponse (the bot’s real answer)
- evaluationScore
Share the evaluation output with your process owner. The actualResponse column serves as a working draft:
Validate correctness.
Add expected responses.
Refine questions for edge cases.
This turns evaluation into a collaborative workflow instead of a solo task.
Closing thoughts:
By leveraging the agent’s own knowledge base and combining it with Copilot Studio’s Evaluation module, you can turn hours of manual work into a quick, structured process. This hack isn’t just about speed—it’s about creating a collaborative foundation for quality assurance.
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