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
Context Injection Methods
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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 - Introduces the foundational Retrieve-Augmented Generation (RAG) architecture, highlighting the need and methods for integrating retrieved knowledge into language model prompts.
- Prompt Engineering Guide, Prompt Engineering Guide Contributors, 2024 - A comprehensive online resource providing strategies and techniques for designing effective prompts for large language models, including how to structure prompts with external context.
- LangChain Documentation - Prompt Templates, LangChain, 2024 - Official documentation illustrating the use of prompt templates in the LangChain framework, a practical approach for managing and injecting context into LLM prompts.
- Lost in the Middle: How Language Models Use Long Contexts, Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, Percy Liang, 2023 Transactions of the Association for Computational Linguistics (TACL) DOI: 10.48550/arXiv.2307.03172 - This research investigates how the placement of information within long contexts affects language model performance, specifically discussing phenomena like 'recency bias' relevant to context injection.