RAG
Retrieval-augmented generation for enterprise knowledge applications.
Maturity level: L2 — Can Build
Six perspectives on RAG
Roadmap
Learn after LLM fundamentals and Python APIs.
Architecture
Sits between ingestion, vector DB, reranker, and LLM in GenAI systems.
Company
Among the most requested GenAI capabilities in analyzed postings.
Projects
Build enterprise doc Q&A with eval metrics.
Interview
Expect architecture, evaluation, and failure mode depth.
Career
Central to LLMOps and GenAI engineer paths.
What & Why
What: Pattern combining retrieval from a knowledge base with LLM generation.
Why: Grounds LLM responses in private data without full fine-tuning.
Build this
Production RAG system with evaluation harness and citation tracking.
Production reality
- ! Bad chunking
- ! Stale indexes
- ! Hallucination despite RAG
- ! Latency spikes
- ! Cost blowups
Interview preparation
- RAG vs fine-tuning tradeoffs
- How do you evaluate RAG quality?
- Chunking strategies
Failure scenario: RAG Hallucination Despite Retrieval
- Symptom
- Answers cite wrong documents or invent facts
- Root cause
- Poor chunking strategy or insufficient reranking
- Permanent fix
- Improve chunking, add reranker, implement evaluation harness
Connected skills
Explore RAG in the interactive universe or train with live cohorts.