RAG (Retrieval-Augmented Generation)
A technique that grounds LLM responses in external data by retrieving relevant documents at query time and injecting them into the prompt context.
RAG solves the core problem with Large Language Models: they don't know about your private data, and their training data has a cutoff date. Instead of retraining the model (expensive) or hoping it knows the answer (unreliable), RAG retrieves the relevant information on the fly.
The typical RAG pipeline has three stages. First, your documents are chunked and converted into vector embeddings, then stored in a vector database. Second, when a user asks a question, their query is also embedded and used to find the most semantically similar document chunks. Third, those retrieved chunks are injected into the LLM prompt as context, grounding the response in your actual data.
Production RAG systems add layers of sophistication: hybrid search combining vector similarity with keyword matching, re-ranking retrieved results with cross-encoder models, query transformation to handle ambiguous questions, and metadata filtering to scope results. The quality of your chunking strategy and embedding model often matters more than which LLM you use.
Related Terms
Embeddings
Dense vector representations of text, images, or other data that capture semantic meaning in a high-dimensional space, enabling similarity search and clustering.
Vector Database
A specialized database optimized for storing, indexing, and querying high-dimensional vector embeddings with sub-millisecond similarity search.
LLM (Large Language Model)
A neural network trained on massive text corpora that can generate, understand, and transform natural language for tasks like summarization, classification, and conversation.
Fine-Tuning
The process of further training a pre-trained LLM on a domain-specific dataset to specialize its behavior, style, or knowledge for a particular task.
Prompt Engineering
The practice of designing and iterating on LLM input instructions to reliably produce desired outputs for a specific task.
Transformer
The neural network architecture behind modern LLMs, using self-attention mechanisms to process and generate sequences of tokens in parallel.
Further Reading
5 Common RAG Pipeline Mistakes (And How to Fix Them)
Retrieval-Augmented Generation is powerful, but these common pitfalls can tank your accuracy. Here's what to watch for.
Vector Databases Compared: Pinecone vs Weaviate vs Qdrant vs Milvus
Choosing the right vector database for your AI application matters more than you think. I've run production workloads on all four—here's what actually performs, scales, and costs in 2026.