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What Is a Vector Database? Embeddings, Similarity Search, and RAG
If you are building with LLMs, you will eventually run into a simple question: what is a vector database, and why does everyone use one for RAG? The short answer is this: a vector database stores numerical representations of text, images, or other data so you can find items by meaning, not just by exact keywords.…
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Production RAG Architecture: Citations, Caching, Evaluation & Guardrails
Building a RAG prototype took me a couple of weeks. Shipping it to real users took months. The gap between those two milestones was filled with everything I had not thought about: users who needed to verify answers, response times that were too slow for real workflows, quality that drifted without anyone noticing, and edge cases…
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How to Improve RAG Quality: Chunking, Retrieval, and Reranking That Actually Work
I built a RAG prototype, tested it with a handful of documents, and felt good about it. Then real users showed up. The questions got messier, the documents got longer, and the answers started drifting. One user asked about our refund policy and got instructions for resetting their password. Another asked about API rate limits and got a…