A Deep Dive into Building a Production-Level RAG System
Learn how to build a Retrieval Augmented Generation (RAG) system from scratch to combat LLM hallucinations and provide accurate, context-aware answers.
This video provides a comprehensive guide to understanding and building a production-level Retrieval Augmented Generation (RAG) system. Watch the full video below:
This video tackles the common problem of "hallucinations" in Large Language Models (LLMs) and presents RAG as a powerful solution. Instead of costly fine-tuning, RAG provides LLMs with the right context at the right time.
Key Concepts Covered:
Understanding RAG: The video explains how RAG enhances LLM responses by retrieving relevant information from a knowledge base, ensuring answers are accurate and grounded in data.
Two-Phase Architecture: It details the two main phases of a RAG system: the one-time Indexing Phase for processing and storing documents, and the real-time Query Phase for answering user questions.
Indexing Deep Dive: Learn about the crucial steps of document loading, chunking text for better retrieval, creating vector embeddings, and storing them in a vector database.
Querying with Context: The video demonstrates how to handle user queries, including follow-up questions, by transforming and embedding the query to find the most relevant information to augment the LLM's prompt.
Preventing Hallucinations: A key takeaway is how to instruct the LLM to answer only based on the provided context, and to state when an answer cannot be found.
Technologies Used:
The project in the video utilizes a modern stack for building RAG applications:
LangChain.js: For simplifying the development process.
Pinecone: As the vector database for efficient semantic search.
Google Generative AI (Gemini): For both embedding and generation tasks.
This video is a must-watch for anyone looking to build more reliable and accurate applications with Large Language Models.