Talking the Talk: Measuring Chatbot Accuracy
summary
Defines concrete accuracy targets and a runnable test stack: NLU metrics (intent accuracy, entity precision/recall, out‑of‑scope detection), conversational metrics (dialog success, multi‑turn retention), and response quality (semantic similarity, sentiment, perplexity). Provides benchmarks—perplexity ~10 for humans, perplexity >100 = poor—and a TypeScript/Playwright framework wired to LM Studio and a llama‑index model with transformers, cld3, axios and Allure. Demo uses hardcoded JSON cases, checks semantic similarity ≥0.5 and shows practical automation: framework is built and exercised.
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chapters (11)
- 0:00Chatbot accuracy intro
- 7:34Why accuracy matters
- 11:55Natural language understanding metrics
- 17:22Perplexity score
- 21:50Conversational metrics
- 24:44Response quality metrics
- 31:20Error handling strategies
- 33:55Advanced evaluation
- 37:03Improving accuracy
- 39:58Building accuracy framework
- 44:58Q&A
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