RAG Knowledge Search: From Architecture to Quality Evaluation
RAG (Retrieval-Augmented Generation) is information retrieval from corporate documents with natural language answer generation. This page covers architecture, quality evaluation, and costs.
Discuss a RAG solutionWhat is RAG
RAG is an architectural pattern: documents are split into chunks, converted to vector embeddings, and indexed. On user query, the system finds relevant fragments and passes them to an LLM for answer generation with source citations.
RAG system architecture
Components: document pipeline (parsing, chunking, embeddings), vector DB (Pinecone/Qdrant/pgvector), retriever (query search), LLM (answer generation), post-processing (citation, filtering).
Security and access control
Documents often contain confidential information. The RAG system must inherit access policies: users only see documents they have rights to. Implemented through metadata filters during search.
RAG quality evaluation
Key metrics: precision@k (share of relevant chunks in top-k), recall (coverage completeness), answer correctness, faithfulness (answer-to-source alignment). Evaluated on a manual sample of 50-100 questions.
Cost and ROI
Typical RAG pilot: from 150,000 RUB for 2-3 weeks. Savings: 40-60% time on information search. With 10+ employees spending 1-2 hours daily searching, payback is 2-4 months.
Related Cases
FAQ
RAG or fine-tuning — which to choose?
RAG — when you need search over updatable documents. Fine-tuning — when you need to change model response style or format. In most corporate scenarios, RAG is more effective and cheaper.
What documents can be indexed?
PDF, DOCX, XLSX, HTML, Markdown, Confluence, Notion, Google Docs. OCR supported for scans. Pipeline is customized to client format.
Can it be deployed on-premise?
Yes. Vector DB, embedding model, and LLM can run in a closed environment. We use open-source models (Llama, Mistral) for on-prem scenarios.
Ready to discuss?
Leave a request — we'll audit your process, calculate ROI, and propose a pilot scenario.
Discuss a RAG solution