6.4. Reflection Checkpoint

Key Takeaways

• LLM-first text analysis lets one generative model classify, extract, and summarize — but you must constrain output (enum/schema, low temperature) for reliability.
• Schema-valid is not fact-valid; validate even structured results.
• The dedicated Azure Language service wins for guaranteed PII redaction, high-volume/low-cost batch NLP, and specialized tasks (health) — often as a preprocessing step before an LLM.
• The privacy pattern is redact first, then send to the model — never hand raw PII to an LLM and rely on instructions.

Connecting Forward

That completes the six content domains. The terminal phases ahead — Exam Readiness, Glossary, and Conclusion — tie the decision filters and traps from all six domains into exam-day strategy, a cross-referenced glossary, and a confidence checklist. The four decision filters from Phase 1 (boundary, grounding, agent workflow, modality) remain your triage for any scenario across every domain.

Self-Check Questions

• For each, choose LLM-first or dedicated Language service and justify: (a) redact PII from 5 million transcripts, (b) classify 200 survey responses into evolving ad-hoc themes, (c) extract clinical entities from medical notes.
• Why does "ask the model to classify into our labels" risk out-of-set answers, and what two prompt-design changes from Phase 2 make the output reliable?