Why RAG?
Large language models (LLMs) like GPT-4 can produce fluent, grammatically accurate text; however, without access to external, updated knowledge, they frequently hallucinate or fabricate facts. This turns into a prime issue in high-stakes environments — like legal, medical, or business enterprise contexts — in which accuracy and accept as true with are non-negotiable.
Retrieval-augmented generation (RAG) resolves this problem by fetching relevant, trusted information from your own knowledge base (e.g., documents, PDFs, internal databases) and injecting it into the LLM prompt. This method grounds the model`s outputs, dramatically lowering hallucinations whilst tailoring responses to your domain.