RAG Systems
35 min read
RAG Fundamentals
Why retrieval matters
Large language models have a problem: they only know what was in their training data. RAG (Retrieval-Augmented Generation) solves this by giving AI access to external knowledge—your documents, databases, or the latest information.
The Knowledge Problem
ChatGPT's training data has a cutoff date. It doesn't know about recent events. It doesn't know your company's internal documents. It can't access private databases. RAG solves this by retrieving relevant information at query time and providing it as context to the model.
How RAG Works
RAG has three steps: 1) Convert documents into embeddings (number representations), 2) When a user asks a question, find the most relevant document chunks, 3) Include those chunks in the prompt so the AI can use them. The AI generates answers grounded in your actual data, not just its training.
Why RAG Matters
RAG enables AI that knows your specific domain. Customer support that knows your products. Legal AI that references your case files. Research AI that cites your papers. It's the bridge between general AI and domain-specific applications.
💡 Key Takeaways
- LLMs only know their training data—RAG extends knowledge
- RAG = Retrieve relevant documents → Generate grounded answers
- Enables domain-specific AI applications
- Critical for enterprise AI deployment
Ready for the full curriculum?
This is just one chapter. Get all 10+ chapters, practice problems, and bonuses.
30-day money-back guarantee • Instant access • Lifetime updates