How retrieval-augmented generation (RAG) works, step by step
RAG lets AI answer from your documents. Each stage explained: ingestion, chunking, embeddings, retrieval, reranking, prompting and evaluation, with practical tips.
AI Desk3 min read
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#Embeddings
Every The Latent Daily story, tool, model and resource tagged “Embeddings”.
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Vector databases explained: how they work and how to choose one
Embeddings, similarity search, indexes like HNSW and the trade-offs between pgvector, Pinecone, Weaviate, Qdrant, Milvus and Chroma for your RAG project.
AI Desk3 min read