How can you help an AI give better answers by connecting it to your own data?

Retrieval-Augmented Generation (RAG) is an AI framework that combines information retrieval with text generation to produce more accurate, up-to-date, and verifiable responses from Large Language Models.

The core problem RAG solves: LLMs have knowledge cutoffs, can hallucinate facts, and don't have access to private or specialized information. RAG addresses this by retrieving relevant documents before generation.

A typical RAG pipeline works as follows: Documents are preprocessed (chunked into appropriate sizes), embedded using an embedding model (converting text to numerical vectors), and stored in a vector database. At query time, the user's question is embedded, similar chunks are retrieved via vector similarity search, these chunks are combined with the question in a prompt, and the LLM generates a response grounded in the retrieved context.

Key components include: chunking strategy (overlap, semantic boundaries), embedding model choice (affects retrieval quality), vector database (Pinecone, Weaviate, pgvector), retrieval method (similarity search, hybrid search, reranking), and prompt construction (how you present retrieved context to the LLM).

Challenges include: retrieving the right chunks (relevance vs coverage), handling context window limits, maintaining retrieval quality as knowledge base grows, and balancing retrieval latency with quality.

RAG is fundamental for building production LLM applications that need access to current, private, or domain-specific information while maintaining response accuracy.