Embeddings and semantic search
What an embedding is, how cosine similarity finds texts that mean the same thing, how to create embeddings with Voyage AI, and where to store them.
AI: knowledge and RAG
AI series 2 of 5. How to give a model access to your own knowledge. Embeddings, semantic search, RAG and diagnosing failures.
4 parts
- Part 1
- Part 2
RAG: retrieval-augmented generation
The full RAG pipeline: when you need it, splitting documents into chunks, retrieving the closest ones, and building a prompt that answers only from them.
- Part 3
Better retrieval: hybrid search, reranking and contextual chunks
Three techniques that fix most RAG retrieval failures: combining keyword and vector search, reranking the candidates, and adding context to every chunk.
- Part 4
Agentic RAG and GraphRAG
Two steps beyond a fixed RAG pipeline: letting the model search as a tool, as many times as it needs, and building a knowledge graph for questions that span many documents.