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Akshay Thakare

RAG

Retrieval-augmented generation: chunking, embeddings, retrieval and evaluation.

5 notes · all topicsGenAI & LLMs

Start here · beginner2

  1. 1Embeddings and Vector Search, Explained for EngineersWhat an embedding is, how similarity is measured, why vector databases use approximate search, and when to add keyword search alongside it.
  2. 2RAG Pipeline FundamentalsIngestion, chunking, embeddings, retrieval and generation — the parts of a retrieval-augmented generation system and where each one fails.

Next · intermediate2

  1. 3Chunking Strategies for RetrievalFixed-size, recursive, structure-aware and parent-child chunking — and how to choose based on your documents and questions.
  2. 4Hybrid Search and Reranking for Better RAGWhy vector search alone misses exact terms, how to combine it with keyword search, and how a reranker improves the final top results.

Go deeper · advanced1

  1. 5Evaluating a RAG System: Retrieval and AnswersMeasure retrieval and generation separately: recall@k, MRR, groundedness and answer correctness, with an evaluation set you build from real questions.