What is
RAG?
Retrieval-Augmented Generation: an LLM technique where the model retrieves relevant documents before generating a response.
Definition
Retrieval-Augmented Generation combines a search step with a generation step. Instead of relying purely on the model's training data, the system retrieves relevant chunks from a corpus (your docs, your support tickets, your codebase) and includes them in the prompt. This grounds the answer in your actual data and dramatically reduces hallucinations. Modern RAG uses hybrid retrieval (vector similarity + keyword search), reranking, and citation traceability so every answer points back to a source.
Example
A legal-tech app where the LLM cites the exact case-law paragraph it used to answer a question.
How Vedwix uses RAG in client work
Most production AI features we build start as RAG systems. We use hybrid retrieval, rerankers, and a citation layer so users can audit every answer.
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If you're building with RAG in production, we can help — from architecture review to full implementation.
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