#embeddings

4 RAG embedding models compared in 2026: Nemotron 3 Embed vs OpenAI, Cohere and...

IVF's cluster-and-probe vs HNSW's small-world graph — recall, memory, build time, and how each lands in pgvector and FAISS.

RAG (Retrieval Augmented Generation) is the breakthrough technique that makes AI systems dramatically more accurate, up-to-date, and useful for real-world appli

I moved a vault search index from 1,536 to 768 dimensions. Here is why I rebuilt all 5,383 chunks instead of mixing vector spaces.

Production RAG patterns are what separate the impressive demo from the system that actually answers customer questions correctly at 3am. Most teams ship a naive

An embedding tour makes one limit visible: a radically different projection can preserve the same cosine-neighbour geometry.

Voyage AI is Anthropic's recommended embeddings provider for Claude RAG. voyage-3 outperforms OpenAI at 50% cost. Setup, models, multilingual, reranker.

Claude + Pinecone RAG: chunking, embeddings (Voyage AI), vector search, reranking, answer generation. 5-step pipeline with cost: ~$0.001 per query.

Vector database patterns determine whether your AI feature returns relevant results in 50ms or a confused mess in 800ms. The vector database market matured rapi

An embedding is a []float32. A store of a million of them is 512 MB of float32 that you will spend...

Stop Using Top-K Retrieval. Try This Instead. Everyone talks about RAG like the hard part...

An LLM can only reason about information inside its context window. It doesn't automatically know my...