Cadre

Capstone 08 — Production RAG Chatbot for a Regulated Vertical

Harvey, Glean, Mendable, and LlamaCloud all run the same production shape in 2026. Ingest with docling or Unstructured and ColPali for visuals. Hybrid search. Re-rank with bge-reranker-v2-gemma. Synthesize with Claude Sonnet 4.7 using prompt caching at 60-80% hit rate. Guard with Llama Guard 4 and NeMo Guardrails. Watch with Langfuse and Phoenix. Grade with RAGAS on a 200-question golden set. Build one in a regulated domain (legal, clinical, insurance), and the capstone is passing the golden set, the red team, and the drift dashboard.

Capstone 30 hr Python (pipeline + API), TypeScript (chat UI)

Prerequisites: Phase 5 (NLP), Phase 7 (transformers), Phase 11 (LLM engineering), Phase 12 (multimodal), Phase 17 (infrastructure), Phase 18 (safety)

#Problem

Regulated-domain RAG (legal contracts, clinical trial protocols, insurance policies) is the most-shipped production shape of 2026 because the ROI is obvious and the stakes are concrete. Harvey (Allen & Overy) built it for legal. Mendable ships the developer-docs flavor. Glean covers enterprise search. The pattern is: ingest high-fidelity, retrieve hybrid with rerank, synthesize with citation enforcement and prompt caching, guard with multiple safety layers, and monitor drift continuously.

The hard parts are not the model. They are jurisdiction-aware compliance (HIPAA, GDPR, SOC2), citation-level auditability, cost control (prompt caching buys 60-90% discount when hit rate is high), hallucination detection via RAGAS faithfulness, and drift detection when the source documents get updated without the index catching up. This capstone asks you to ship all of it on a 200-question golden set with a red-team suite alongside.

#Problem

Keep going. The rest of this lesson is one step away.