The Evolution of RAG
summary
Overview of concrete RAG advances: pipelines move from simple retrieve+LLM to modular systems with a semantic router, rewriter, retriever, reranker, consolidator and long/short-term memory; implementations use Redis, HNSW indexes, BM25/full‑text hybrid search and LangChain-style orchestration; teams build semantic caches and multi-source parallel retrieval that cut latency and token costs and enable domain-specific smaller models. rewriter gives 25–35% lift, cross‑encoders 15–30% more accurate but 5–10× slower, semantic cache returns milliseconds not seconds.
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chapters (11)
- 0:00Introduction to RAG evolution
- 3:14What is RAG and its origins
- 8:34Problems with LLMs and RAG motivation
- 13:10Retrieval in RAG systems
- 16:41Vector embeddings and indexing
- 27:59Naive RAG explained
- 37:37Advanced RAG techniques
- 45:25Semantic caching in RAG
- 46:50Modular RAG architecture
- 50:05From RAG to agentic systems
- 57:50Future of RAG and multimodal systems
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