LLMs cannot memorize long-tail facts, their knowledge goes stale, and they cannot see private documents. The RAG slides of NTU ADL Fall 2025 open with an LLM hallucinating about the lecturer herself, then split RAG into indexing, retrieval, and generation: sparse (TF-IDF, BM25) and dense (DPR, Contriever) retrieval, how dense retrievers are trained, pre- and post-retrieval techniques including pointwise and pairwise reranking. A closing roadmap organizes RAG, RETRO, FLARE, Search-R1, and others by what, how, and when to retrieve. For HW3, only the title is public: "Retriever & Reranker Training for RAG".
RAG has evolved far beyond simple 'search + generate' into a technology ecosystem spanning ten generations — and since 2025 into an Agentic/Reasoning era. This article is a systematic navigation guide: from Naive RAG to Multi-Agent/LongRAG across ten generations, the post-ten Agentic Era (Search-R1/RL search, MCP, GraphRAG 3.x, vision-native retrieval), retrieval strategies, chunking, embedding, reranking, evaluation frameworks, observability, and cost optimization. Each topic has a dedicated deep-dive article.
Bi-Encoders are too coarse, Cross-Encoders are too slow — ColBERT's Late Interaction finds the sweet spot: token-level comparison between query and document, but with document vectors that can be precomputed.
Vector search similarity scores don't equal relevance. Cross-Encoders use pairwise comparison to reorder results and push the truly relevant documents to the top.
Ranking purely by relevance leaves you with five documents all describing the same route. MMR strikes a balance between relevance and diversity, and layering in popularity weighting makes results even more useful.