RAG: Retrieval-Augmented Generation
RAG doesn't solve the problem of "models can't speak"; it solves the problem that "models may not have the latest, most accurate, or most private information". It first searches an external knowledge base for relevant snippets, then feeds these snippets to the model to generate answers, making responses more "evidence-based" rather than "memory-based".
Ask First
Retrieve Documents
Generate with Sources
Select a Question and Walk Through the RAG Process
After switching questions, click different stages to observe whether the system is "understanding the query", "performing retrieval", "assembling context", or "answering based on context".
Current User Question
With RAG
What RAG Enhances
Latest Information
Model parameters may not contain today's, last week's, or your company's latest internal information, but retrieval systems can access this content in real-time.
Private Knowledge
Company policies, product manuals, and customer documents are typically not in public pre-trained corpora, but can be stored in private knowledge bases.
Traceability
When answers include citation snippets, users can more easily verify "where this came from" instead of blindly trusting the model's memory.
Not a Silver Bullet
RAG can still fail if retrieval is incorrect, chunks are poorly sliced, context is too large, or evidence isn't properly used during generation.
Four Common Stages of RAG Systems
1
Chunking & Indexing
Split long documents into retrieval-friendly chunks and build indexes for them.
2
Retrieval & Ranking
Find the most relevant document snippets based on the user's question.
3
Context Assembly
Organize highly relevant evidence into model-readable context input.
4
Evidence-Based Generation
Generate final answers by combining the question with retrieved context.
Common Misconception: RAG is not "feeding all documents directly to the model". The key is to retrieve first, then filter, then assemble—otherwise context costs and noise will rapidly increase.
Summary: RAG shifts models from "trying to remember" to "searching first, then answering"—a critical step for many applications to move from demo to production.