Embark on a comprehensive deep-dive into Retrieval Augmented Generation (RAG) techniques, from basic implementations to advanced strategies. Learn how to overcome common challenges such as inaccurate or incomplete answers, outdated information, and ineffective text chunking. Explore various search methods including semantic, metadata, full text, and multimodal searches, as well as the importance of embedding model selection. Discover advanced RAG techniques using LangChain4j, including semantic chunking, query expansion and compression, metadata filtering, document reranking, and data lifecycle processes. Gain insights on evaluating and presenting results effectively to users, drawing from the speakers' extensive experience in implementing RAG pipelines across different projects and frameworks. This in-depth conference talk is ideal for developers looking to enhance their RAG implementations and build more accurate, context-aware AI applications.
From Naive to Advanced RAG: The Complete Guide - Lecture 1
Overview
Syllabus
From naive to advanced RAG: the complete guide by Cédrick Lunven, Guillaume Laforge
Taught by
Devoxx
Related Courses
-
RAG Fundamentals and Advanced Techniques - Full Course
-
Learn Embeddings and Vector Databases
-
RAG: From Dumb Implementation to Serious Results - Optimizing Retrieval-Augmented Generation
-
Optimizing RAG Implementations for Specialized Enterprise Tasks
-
Chunking Strategies for Retrieval Augmented Generation (RAG) with LangChain and LlamaIndex
-
Private RAG with Open Source and Custom LLMs - BentoML and OpenLLM
