Title: Outcomes & Rubrics
Course: AIINFRA 301: Production RAG & LLMOps — Observability and Evaluation > Section: 00 · Course Information

AIINFRA 301: Production RAG & LLMOps — Observability and Evaluation — Outcomes & Rubrics

Course Learning Outcomes

1. CLO1 — Select and configure a vector database (Qdrant, Milvus, Weaviate, or pgvector) based on scale, filtering, and indexing tradeoffs.
2. CLO2 — Design chunking and hybrid retrieval pipelines combining dense embeddings, BM25, and cross-encoder reranking.
3. CLO3 — Build automated RAG evaluation suites with RAGAS and DeepEval to gate retrieval and generation quality in CI/CD.
4. CLO4 — Instrument LLM applications with tracing and observability platforms to diagnose latency, cost, and quality regressions.
5. CLO5 — Implement production monitoring, prompt versioning, and feedback loops to detect and respond to model and data drift.

Together, CLO1–CLO5 ladder up to the certificate's program-level outcomes by building the AI infrastructure and architecture competencies — retrieval system design, automated quality gating, observability, and production operations — that California employers expect of job-ready AI infrastructure engineers.

Rubrics