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
Developers & Builders
RAG, Search & Vector DBs
Retrieval-augmented generation, search and vector databases.
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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
Quick take: bigger context windows are not always better
Models now accept huge inputs, but stuffing everything into the prompt can reduce accuracy and raise costs. When long context helps, and when retrieval works better.
AI Desk3 min read