rag

RAG retrieves facts at query time. Fine-tuning bakes behavior into model weights. Use RAG for facts; fine-tune for style or narrow tasks.

Grounding is the practice of tying AI output to verifiable external sources so answers are factual and citable.

A vector database stores embeddings and finds the most similar ones fast. It powers semantic search, RAG, and recommendations.

RAG explained in plain English. Learn how AI answers questions using your own documents — the most popular AI technique in business today.

Replace keyword search with semantic search using embeddings, pgvector, and hybrid BM25 + vector scoring — better results in an afternoon.

Build a searchable, chat-enabled knowledge base from your docs using RAG, pgvector, and a clean chat UI — for internal or customer-facing use.

Ship a Perplexity-style AI search engine using embeddings, RAG, and streaming LLM responses — deployed on your own infrastructure.

Build a production retrieval-augmented generation app with pgvector, embeddings, and any OpenAI-compatible LLM. Covers chunking, reranking, and citation.