Retrieval-Augmented Generation (RAG) is a technique that enhances AI responses by grounding them in external knowledge — your documents, databases, or any data source — rather than relying solely on the model's training data.

The Core Problem

Large language models like GPT-5 and Claude are trained on vast amounts of public internet data. But they don't know:

• Your company's internal documentation and SOPs
• Your product catalog and pricing details
• Recent information published after their training cutoff
• Private data, customer records, or proprietary research
• Domain-specific knowledge not well-represented on the internet

When asked about these topics, models either hallucinate (make up plausible-sounding answers) or admit they don't know.

How RAG Solves This

RAG adds a retrieval step before generation:

1. User asks a question — "What's our refund policy for enterprise clients?"
2. System searches your documents — Finds the relevant section in your policies
3. Retrieved text is injected into the prompt — As context for the AI
4. AI generates a grounded answer — Based on your actual documentation

The result: accurate, up-to-date answers sourced from your own data, with the ability to cite sources.

RAG vs Fine-Tuning

Fine-tuning trains the model on your data, baking knowledge into its weights. RAG keeps your data external and retrieves it at query time.

When to Use RAG

✅ Customer support bots grounded in your help docs ✅ Internal knowledge bases and company wikis ✅ Legal/compliance document search ✅ Technical documentation Q&A ✅ Research assistants over paper collections ✅ Personalized recommendations from product catalogs

When NOT to Use RAG

❌ When the model already knows enough (general knowledge questions) ❌ When you need to change the model's writing style (use fine-tuning instead) ❌ When your documents are poorly structured or very noisy ❌ When real-time structured data is needed (use direct API/SQL instead)