RAG Architecture: Building AI That Knows Your Business
Retrieval-Augmented Generation explained — how it works, when to use it, and how to build it right.
Large language models are powerful but they have a fundamental limitation: their knowledge is frozen at training time. RAG — Retrieval-Augmented Generation — solves this by connecting LLMs to live, authoritative data sources at inference time. The result is AI that gives accurate, up-to-date, source-grounded answers about your specific domain. This is the architecture behind most serious enterprise AI deployments today.
How RAG Works
RAG has two phases. First, an indexing phase: your documents, databases, and data sources are chunked, converted to vector embeddings, and stored in a vector database. Second, a retrieval phase: when a user asks a question, the same embedding model converts the query to a vector, finds the most semantically similar chunks from your index, and injects them into the LLM's context window alongside the query.
The LLM then generates a response grounded in your retrieved context rather than hallucinating from training data. The key insight is that LLMs are excellent reasoning engines — they just need accurate, relevant information to reason over. RAG provides that information at the right moment.
Chunking and Embedding Strategy
The quality of a RAG system is largely determined by retrieval quality — which depends heavily on how documents are chunked and embedded. Naive fixed-size chunking loses semantic coherence. Better approaches include recursive character splitting with overlap, semantic chunking that respects paragraph and section boundaries, and document-specific strategies (e.g., preserving table structure in financial docs).
Embedding model choice matters significantly. General-purpose models like OpenAI's text-embedding-3-large or Cohere's embed-v3 work well broadly. Domain-specific fine-tuned embeddings outperform them in specialized verticals. Benchmark retrieval quality on your actual corpus before committing to an embedding strategy.
Vector Databases and Infrastructure
The vector database is the storage and retrieval engine for your embeddings. Purpose-built options include Pinecone, Weaviate, Qdrant, and Milvus. PostgreSQL with the pgvector extension is a strong choice for teams already running Postgres, avoiding additional infrastructure complexity. Supabase makes pgvector particularly accessible.
Beyond the vector store, production RAG systems need a reranking layer — a second model that re-scores retrieved chunks for relevance before passing them to the LLM. Cohere Rerank and cross-encoder models dramatically improve retrieval precision and are worth the additional latency cost (typically 100–200ms).
Advanced RAG Patterns
Basic RAG degrades on complex queries: multi-hop questions requiring synthesis across documents, ambiguous queries that need clarification, and queries where the relevant context spans many chunks. Advanced patterns address these: HyDE (Hypothetical Document Embeddings) generates a hypothetical ideal answer and uses it as the retrieval query. Multi-query RAG generates multiple reformulations of the question and merges results. Agentic RAG uses an LLM to plan and execute multi-step retrieval strategies.
Evaluate your RAG system rigorously using frameworks like RAGAS, which measures faithfulness (does the answer stick to the retrieved context?), answer relevancy, and context recall. RAG that isn't evaluated isn't trusted — and systems that aren't trusted don't get used.