RAG: why context decides the quality of AI output

A large language model writes impressively, but it does not know your company. It has never seen your price lists, your returns process, or what was agreed with a particular supplier. This is exactly the gap that the approach known as RAG — Retrieval-Augmented Generation — closes.

How RAG works

The principle is simpler than it sounds. Documents — internal policies, product descriptions, emails, documentation — are split into smaller chunks, and each is turned into a vector, a numerical representation of its meaning. When a question arrives, the system finds the most relevant chunks by vector similarity and adds them to the prompt alongside the question. The model then answers from concrete material it was given, not from memory.

• Indexing: data is chunked and stored in a vector database (for example pgvector on PostgreSQL).
• Retrieval: the passages closest in meaning to the question are pulled.
• Generation: the model answers and can cite its source directly.

Why it matters

Without relevant context a model "fills in" plausible words, and that is how hallucinations arise. With context, the answer rests on verifiable material that can be checked. That is the difference between an impressive demo and a tool you can trust in production.

When to use it

RAG makes sense wherever answers depend on your own, frequently changing data: customer support, internal search, assisted document processing. For fixed-rule facts it is often cheaper and more reliable to write ordinary logic. The line between what to hand to the model and what to leave to deterministic code is drawn by a good understanding of context — and that is the heart of our approach.