Section 164 · Chapter 20, The Practical Playbook
Testing a Chatbot
Chatbots need more than answer checks. Confidence Engineers must evaluate multi-turn behavior, grounding, safety, tone, memory, escalation, and recovery.
escalationretrievalrefusalconfidence engineerchatbot
What to do
- Start with intent coverage.
- Build a sample of common intents, high-risk intents, ambiguous intents, out-of-scope requests, frustrated users, and adversarial users.
- Treat those as separate slices because a chatbot can be excellent in one category and dangerous in another.
- Test whether the bot carries useful context forward without clinging to stale or wrong context.
- Treat the conversation trace as the artifact under test.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for escalation, retrieval, refusal, confidence engineer needed to reproduce work on Testing a Chatbot.
- Report results for escalation, retrieval, refusal, confidence engineer by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Chatbot testing should combine transcript-level rubrics, turn-level annotations, retrieval checks, tool-call checks, adversarial prompts, memory isolation tests, and production conversation sampling. Treat the conversation trace as the artifact under test.
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Cite this page
Jason Arbon. "Testing a Chatbot." Testing AI Knowledge Edition, section 164.
https://jarbon.ai/testing-ai/knowledge/ch164-chatbot.html