One of the most common questions we receive from business leaders is: "How do we get AI to answer questions based on our own documents, not just its general training?" The answer is Retrieval-Augmented Generation, or RAG — a technique that combines a language model's reasoning capabilities with a searchable index of your organization's specific knowledge.
Why not just paste documents into the prompt?
For small document sets, you can. But most organizations have thousands of documents — policy manuals, product documentation, customer communications, research reports — far too many to fit in any model's context window. RAG solves the selection problem: instead of sending all documents to the model, it retrieves only the most relevant passages for a given query and sends those.
How RAG works, without the jargon
The process has two phases. In the indexing phase, your documents are broken into chunks (typically paragraphs or sections), and each chunk is converted into a vector — a list of numbers that encodes the chunk's semantic meaning. These vectors are stored in a vector database alongside the original text they represent.
In the retrieval phase, when a user asks a question, that question is also converted to a vector. The vector database finds the document chunks whose vectors are most similar (semantically related) to the question vector and returns those chunks. The language model then uses those chunks as context to formulate its answer.
Vector databases: your options
Pinecone is the most mature managed vector database service — simple API, good scaling characteristics, and solid documentation. It's the right choice for teams that want to get started quickly without infrastructure management overhead.
Weaviate and Qdrant are strong open-source alternatives with self-hosted deployment options, important for organizations with strict data residency requirements. Weaviate adds built-in full-text search alongside vector search, which improves retrieval quality for document-heavy use cases.
pgvector is a PostgreSQL extension that adds vector search to a standard relational database. For organizations that already run PostgreSQL and want to avoid introducing a new database technology, pgvector is a pragmatic choice with acceptable performance at moderate scale.
The quality factors that determine RAG performance
Chunk size is the most important tuning parameter: too large and you retrieve irrelevant context; too small and you lose the surrounding context needed for coherent answers. Start with 256–512 tokens per chunk and adjust based on the specific structure of your documents.
Metadata filtering dramatically improves retrieval accuracy: tag your chunks with document type, department, date, and access level, then filter retrieval to relevant subsets. A finance question should retrieve from financial documents; a policy question should retrieve from HR policy documents.
Hybrid retrieval — combining vector similarity search with keyword search — consistently outperforms either approach alone. Build hybrid retrieval from the start rather than discovering this after deployment.
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