RAG

What Is RAG?

Retrieval-Augmented Generation (RAG) is an AI architecture designed to solve the main weakness of large language models (LLMs) — the static training data limit. Before answering a user query, RAG systems retrieve relevant documents or data chunks from a knowledge base in real time and pass this context to the LLM. The model generates its response based on the provided up-to-date data rather than its own "memory".

Core advantages of this approach: Hallucination reduction — The model no longer needs to guess for information outside its training data. Up-to-date knowledge — The knowledge base can be updated without retraining the model. Domain-specificity — Internal company data, proprietary documents, and product catalogues can be directly integrated. Source transparency — The source of the answer can be shown to the user.

Components of RAG Architecture

In Detartech's RAG projects, we use: Document Ingestion — Sources such as PDFs, Word files, HTML, and database records are processed and split into text chunks. Embedding & Vector Store — Chunks are converted to vector representations and stored in a vector database (pgvector, Pinecone, Qdrant). Semantic Retrieval — The user query is vectorised and the most relevant chunks are found via semantic similarity search. Generation — The retrieved context is passed to the LLM together with a system prompt to generate the response.