Getting Started with Retrieve-Augmented Generation (RAG)
Chapter 1: Introduction to Retrieve-Augmented Generation
Limitations of Standard Large Language Models
What is Retrieve-Augmented Generation (RAG)?
The Core Architecture of a RAG System
RAG vs. Fine-tuning: Understanding the Differences
Benefits of Using RAG
Quiz for Chapter 1
Chapter 2: The Retrieval Component
Role of the Retriever in RAG
Introduction to Vector Embeddings
Common Embedding Models
Similarity Search: Finding Relevant Vectors
Introduction to Vector Databases
Choosing a Vector Database: Considerations
Practice: Generating Text Embeddings
Quiz for Chapter 2
Chapter 3: Preparing Data for Retrieval
Loading Documents from Various Sources
The Need for Document Chunking
Fixed-Size Chunking Strategies
Content-Aware Chunking Approaches
Metadata Association with Chunks
Storing Processed Data in a Vector Database
Hands-on Practical: Chunking Documents
Quiz for Chapter 3
Chapter 4: The Generation Component and Augmentation
Role of the Generator (LLM) in RAG
Structuring Prompts for RAG
Context Injection Methods
Managing Context Length Limitations
Generating the Final Response
Attributing Sources in Generated Output
Quiz for Chapter 4
Chapter 5: Building a Basic RAG Pipeline
Overview of RAG Frameworks (e.g., LangChain, LlamaIndex)
Setting up the Environment
Implementing the Retriever
Implementing the Generator Integration
Combining Retrieval and Generation
Running Queries Through the Pipeline
Hands-on Practical: End-to-End RAG System
Quiz for Chapter 5
Chapter 6: Evaluating and Improving RAG Systems
Challenges in Evaluating RAG
Component-Level Evaluation: Retrieval
Component-Level Evaluation: Generation
End-to-End RAG Evaluation Frameworks
Common Failure Modes
Basic Strategies for Improvement
Practice: Analyzing RAG Output Quality
Quiz for Chapter 6
Role of the Retriever in RAG
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- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela, 2020 Advances in Neural Information Processing Systems (NeurIPS) DOI: 10.48550/arXiv.2005.11401 - The original paper introducing the Retrieval-Augmented Generation (RAG) framework, presenting its architecture and demonstrating the effectiveness of combining a retriever with a generator.
- Dense Passage Retrieval for Open-Domain Question Answering, Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih, 2020 Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) DOI: 10.48550/arXiv.2004.04906 - A seminal work on using dense vector embeddings for efficient and effective passage retrieval, which is fundamental to the 'search' stage described for the RAG retriever.
- Retrieval-Augmented Generation for Large Language Models: A Survey, Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, Haofen Wang, 2024 arXiv preprint DOI: 10.48550/arXiv.2312.10997 - A comprehensive survey of Retrieval-Augmented Generation, covering various retriever designs, optimization methods, and their influence on the overall RAG system performance.