Day 8 of 14 35 minChunkingIndexingTop-k search
The first half of retrieval-augmented generation. Split documents into chunks, index them, find the most relevant chunks for a question with keyword, vector and hybrid search, and measure how well retrieval works.
By the end of today you can
- Explain what RAG is and why it beats putting knowledge in the prompt by hand
- Split documents into useful chunks, with overlap and metadata
- Retrieve the top-k chunks for a question with BM25 and with embeddings
- Explain hybrid search and reranking
- Measure retrieval quality with recall at k
What's inside
- 4 sections
- 3 diagrams and mind maps
- 4 code examples you can run
- 5-question quiz
- 1The RAG pipeline
- 2Step 1: chunking
- 3Step 2: retrieval
- 4Step 3: measure retrieval
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