What Is Retrieval Augmented Generation (RAG)?
RAG is the technology that makes AI assistants actually useful for specific domains.
The Problem RAG Solves
Large Language Models (LLMs) like GPT-4 have a problem:
- Training data has a cutoff date
- They don't know your specific information
- They can hallucinate (make things up)
RAG solves this by giving the AI relevant information before it responds.
How RAG Works
1. Document Processing
Your documents are split into chunks and converted to vectors (numbers that capture meaning).
2. Storage
These vectors are stored in a vector database for fast retrieval.
3. Query
When a user asks a question:
- The question is converted to a vector
- Similar content is retrieved
- Relevant chunks are found
4. Generation
The LLM receives:
- The user's question
- Retrieved relevant content
- Instructions on how to respond
5. Response
The AI generates an answer based on your actual content, not just its training data.
Why RAG Matters
Without RAG:
- Generic answers
- Potential hallucinations
- No source attribution
With RAG:
- Specific, accurate answers
- Grounded in your content
- Can cite sources
RAG in Practice
Assisters uses RAG under the hood:
- You upload documents
- We process and store them
- User asks a question
- We retrieve relevant content
- AI generates an accurate answer
You get the benefits without building the infrastructure.
RAG is what makes AI assistants actually know things.