LlamaIndex RAG

LlamaIndex is a framework for building retrieval-augmented generation (RAG) systems. RAG augments large language models with custom data: when a user asks a question, the system retrieves relevant documents from a database (via embedding similarity), feeds them to the LLM, and the LLM generates an answer grounded in those documents. Unlike fine-tuning, RAG is fast to build (days), cheap (no training), and updates instantly (add new documents, they're searchable immediately). LlamaIndex simplifies RAG: it handles document loading, chunking, embedding, retrieval, and LLM prompting. Fine-tuning an LLM takes weeks and costs thousands. RAG takes days and costs hundreds. For most companies, RAG is the right choice. LlamaIndex is the industry standard: it's used by Uber, Stripe, Airbnb, and startups. Learning LlamaIndex unlocks ability to build customer support bots, documentation Q&A, knowledge search, and personalized AI assistants in days. The skill is scarce: most engineers don't know RAG yet. RAG engineers command 30-50k salary premiums.