Retrieval-Augmented Generation for LLM Applications

Bridge the gap between traditional information retrieval systems and innovative large language models (LLMs). With this comprehensive guide, data scientists, ML engineers, and ML/AI architects will explore the integration and mutual enhancement of information retrieval and LLMs. You'll focus on the applications of LLM and retrieval-augmented generation (RAG) technologies for information retrieval.

Authors Wendy Ran Wei, Ling Huang, and Jay Jianqiang Wang demonstrate how to enhance retrieval systems by incorporating external databases with LLMs. You'll begin with the basics of LLMs, information retrieval principles, and RAG's significant impact on information retrieval. You'll then delve into LLM evaluation, cutting-edge developments, and the integration of LLMs with enterprise data for sophisticated search, recommendation, and AI assistants solutions.

Understand the fundamental principles crucial for leveraging LLM and RAG in advanced search and information retrieval systems
Master RAG's intricacies and learn retrieval-based generative techniques for AI assistants
Learn evaluation methods for LLM and RAG, establish benchmarks for measuring accuracy and efficiency, and follow comprehensive guidelines for compliance
Create LLM and RAG-based search engine and recommendation systems for leveraging LLM model representations and RAG robust retrieval and ranking mechanisms
Develop customized AI assistants using pre-trained GPT models
Implement custom chatbots that interact with users to enhance customer support and task automation, and deliver personalized experiences