AI & Data
RAG (Retrieval-Augmented Generation)
Retrieval-Augmented Generation (RAG) is a technique that improves large language model answers by first retrieving relevant documents from a knowledge source and supplying them to the model as context before it responds.
How RAG works
A RAG system has two stages. At ingestion time, documents are split into chunks, converted into embeddings, and stored in a vector database. At query time, the user's question is embedded, the most similar chunks are retrieved, and those chunks are placed into the model's prompt along with the question. The model then answers using the retrieved material rather than only its training data. Production systems add refinements: hybrid search combining keywords and vectors, reranking retrieved results, metadata filtering by department or date, and citations so users can verify where an answer came from.
Why it matters for business software
Language models know nothing about your company's contracts, policies, or product data, and they can fabricate answers when asked anyway. RAG fixes both problems: it grounds responses in your actual documents and makes answers traceable to sources. It is also far cheaper and faster to update than retraining a model, since adding knowledge means indexing new documents, not fine-tuning. This makes RAG the default architecture for internal knowledge assistants, customer support bots, and any application where wrong answers carry real cost.
How Wizcoder AI Labs uses it
RAG is the foundation of most chatbots and knowledge assistants we build. We handle the full pipeline: document ingestion, chunking strategy, retrieval tuning, and evaluation, as part of our AI development services, so answers stay grounded in client data with citations.
Related terms
Where we use RAG (Retrieval-Augmented Generation)
Put the right stack to work
Wondering whether RAG (Retrieval-Augmented Generation) fits your project? A free discovery session gets you an honest answer and a clear plan.
- Free discovery session
- NDA available
- Reply within one business day