RAG Systems (Retrieval Augmented Generation)

RAG combines the power of vector search with Large Language Models to create AI systems that can answer questions using your own data. It's the architecture behind ChatGPT plugins, AI assistants, and enterprise knowledge bases.

What You'll Learn

• RAG Fundamentals: How retrieval augments LLM responses
• Building Your First RAG: Step-by-step implementation guide
• Advanced Patterns: Hybrid search, re-ranking, and query optimization
• Production Deployment: Scaling, caching, and monitoring RAG systems
• Evaluation: Measuring accuracy, relevance, and quality

Why RAG?

Large Language Models have a knowledge cutoff and can hallucinate. RAG solves this by:

• Grounding LLM responses in your actual data
• Providing up-to-date information beyond training cutoff
• Reducing hallucinations with source attribution
• Enabling domain-specific AI without fine-tuning
• Maintaining data privacy and control

RAG Architecture

User Query → Embedding → Vector Search → Retrieved Context → LLM → Response

Common Use Cases

• Customer Support: AI agents with access to docs/tickets
• Internal Knowledge: Company-wide Q&A systems
• Research Assistants: Query academic papers and research
• Code Assistants: Search codebases and documentation
• Legal/Compliance: Navigate regulations and contracts

Explore the articles below to master RAG systems.