Table of Contents
Preface
Part 1 – Introduction to Retrieval-Augmented Generation (RAG)
1
What Is Retrieval-Augmented Generation (RAG)
Understanding RAG – Basics and principles
Advantages of RAG
Challenges of RAG
RAG vocabulary
LLM
Prompting, prompt design, and prompt engineering
LangChain and LlamaIndex
Inference
Context window
Fine-tuning – full-model fine-tuning (FMFT) and parameter-efficient fine-tuning (PEFT)
Vector store or vector database?
Vectors, vectors, vectors!
Vectors
Implementing RAG in AI applications
Comparing RAG with conventional generative AI
Comparing RAG with model fine-tuning
The architecture of RAG systems
Summary
2
Code Lab – An Entire RAG Pipeline
Technical requirements
No interface!
Setting up a large language model ...
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