RAG (Retrieval-Augmented Generation)
Definition
RAG is a technique that enhances LLM responses by retrieving relevant documents from a knowledge base and including them as context, reducing hallucinations and enabling accurate answers about private or recent data.
Why It Matters
RAG solves the biggest limitation of standalone LLMs: they only know what was in their training data. When users ask about your company’s internal documentation, recent events, or domain-specific knowledge, a vanilla LLM either hallucinates or admits ignorance.
RAG bridges this gap by giving the LLM real-time access to your data. Instead of relying solely on memorized knowledge, the model receives relevant documents alongside each query. This means accurate answers about your specific domain without expensive fine-tuning or constant model retraining.
For AI engineers, RAG is foundational. Most enterprise AI applications need to answer questions about private data, support tickets, product documentation, legal contracts, medical records. RAG makes this possible while keeping the LLM’s general reasoning capabilities intact.
Implementation Basics
A RAG pipeline has three core components:
1. Document Processing Your source documents (PDFs, web pages, databases) get split into chunks and converted to embeddings, numerical vectors that capture semantic meaning. These vectors get stored in a vector database for fast similarity search.
2. Retrieval When a user asks a question, convert it to an embedding and find the most similar document chunks. This typically uses cosine similarity or approximate nearest neighbor search. The top 3-10 chunks become your context.
3. Generation Construct a prompt that includes the retrieved documents plus the user’s question. The LLM reads this context and generates an answer grounded in your actual data rather than its training knowledge.
The key engineering decisions: chunk size (too small loses context, too large wastes tokens), retrieval count (more context vs. noise), and prompt structure (how to present documents to the LLM).
Start simple. A basic RAG implementation with sensible defaults often outperforms complex architectures. Add sophistication (reranking, query expansion, hybrid search) only when you’ve identified specific failure modes.
Source
RAG combines pre-trained parametric memory (language model) with non-parametric memory (retrieved documents) for knowledge-intensive NLP tasks.
https://arxiv.org/abs/2005.11401