Real-world test revealed the LLM extractor (gpt-4o-mini) was using type
labels too loosely: a customer's QUESTION about parking ("tem
estacionamento?") was classified as 'reclamacao'. Similarly cortesia
generica ("obrigado") was becoming 'feedback_positivo', and transactional
events (CPF informed, reservation made) were becoming memories when they
should be ignored.
Rewrote build_prompt with:
- Per-type strict definition (what it IS)
- YES/NO examples for each of the 9 types, with the most common pitfalls
explicitly shown as NO
- 7 absolute rules, including: questions are never complaints, generic
courtesy is never feedback, agent actions are never customer memory,
transactional events are not long-term facts
- Confidence threshold guidance (>=0.9 only if totally explicit, 0.7-0.89
for strong inference, <0.7 discard)
- "If in doubt, discard — quality > quantity. Most transactional
conversations should return empty facts list"
Existing 9 specs still pass (stub call_llm, so prompt changes don't
affect unit test assertions).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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