Retrieval Augmented Generation (RAG)
Understand and implement RAG to ground LLM responses in external, up-to-date information, improving accuracy and reducing hallucinations.
What is RAG?
Welcome! In this lesson, we'll dive into Retrieval Augmented Generation (RAG). It's a powerful technique that helps Large Language Models (LLMs) give more accurate and up-to-date answers.
Think of it as giving an LLM a personal research assistant before it answers your question. This assistant quickly finds relevant information from a trusted source.
LLMs: Smart, but Limited
Traditional LLMs are trained on vast amounts of data, but this data has a cut-off date. This means they can't know about recent events or specific, private information.
Without external help, LLMs might:
- Hallucinate: Make up facts that sound plausible but are incorrect.
- Provide outdated info: Give answers based on old data.
- Lack domain-specific knowledge: Struggle with highly specialized topics.
All lessons in this course
- Retrieval Augmented Generation (RAG)
- Function Calling & Tool Use
- Building Simple LLM Agents
- Streaming LLM Responses to Users