Knowledge-Base Search (RAG)

Problem

Keyword search across a large knowledge base returns noisy results and misses answers phrased differently than the query.

Solution

A retrieval-augmented generation system that embeds documents into a vector index and grounds LLM answers in the retrieved passages — accurate, source-backed answers instead of keyword matches.

Overview

Traditional keyword search over a knowledge base fails the moment a question is phrased differently than the source document — it returns noise, or nothing.

How it works

Documents are chunked and embedded into a vector index. At query time, the system retrieves the passages most semantically relevant to the question — not just keyword matches — and feeds them to an LLM as grounding context, so the model answers from the retrieved text rather than from memory. This retrieval-augmented generation (RAG) approach keeps answers traceable back to a real source passage.

Result

Search quality shifts from “matches the words” to “answers the question,” with every answer backed by a retrievable source.