An LLM does not know your data — RAG solves this
An LLM knows only what it learned and does not know your data: internal documents, knowledge base, fresh information. Ask it about your product — it will invent. RAG (Retrieval-Augmented Generation) solves this: it first finds relevant fragments in your data, then gives them to the LLM to answer based on the found. This is a key technique for AI working with your knowledge.
The problem RAG solves
- The LLM does not know your data: company documents, products, rules.
- The LLM invents (hallucinates): answering about what it does not know.
- Data changes: the LLM does not know the fresh.
- You cannot "stuff it all into the prompt": there is too much data for the context.
How RAG solves it
Explain the RAG idea in simple words.
RAG works like "smart search + answer". Analogy: imagine an expert given access to a library. Instead of answering from memory (and risking inventing), they: 1) find the needed books/pages in the library for your question, 2) read them, 3) answer based on what they read, with a reference to the source. RAG does the same with an LLM: 1) Retrieval: for your question the system finds relevant fragments in YOUR data (documents, knowledge base). 2) Augmented: the found fragments are added to the LLM prompt as context. 3) Generation: the LLM answers the question RELYING on these fragments rather than general memory. Example: you have 1000 pages of product documentation. A client asks something specific. RAG finds the needed 2-3 paragraphs from the docs and gives them to the LLM, which formulates a precise answer from them. Without RAG the LLM either does not know or invents. The key benefit: answers are based on YOUR real data rather than "knowledge from thin air", plus you can cite the source. This turns an LLM from "a know-it-all that sometimes lies" into "an assistant answering from your documents".
The three parts of RAG
The name spells out the essence: Retrieval (finding the relevant in your data) + Augmented (augmenting the prompt with the found) + Generation (the LLM generating an answer based on it). First we find the needed, then generate an answer based on it. This combines the LLMs power (understanding, phrasing) with your data (current, private, extensive).
Course rule: RAG connects an LLM with your data — finds relevant fragments and answers based on them. This solves the LLMs ignorance of your data and reduces inventing.
🧠 What does RAG do?