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
Similarity Search: Finding Relevant Vectors
New · Open Source
Kerb - LLM Development Toolkit
Python toolkit for building production-ready LLM applications. Modular utilities for prompts, RAG, agents, structured outputs, and multi-provider support.
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- Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks, Nils Reimers and Iryna Gurevych, 2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (Association for Computational Linguistics (ACL)) DOI: 10.18653/v1/D19-1410 - This paper introduces Sentence-BERT, a method for generating semantically meaningful sentence embeddings that are highly effective for cosine-similarity based semantic search, a core technique in RAG systems.
- Introduction to Information Retrieval, Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze, 2008 (Cambridge University Press) - A fundamental textbook that provides a thorough explanation of vector space models, cosine similarity, and the theoretical basis of information retrieval, directly relevant to semantic search. Chapter 6 is particularly valuable.
- Approximate Nearest Neighbor Search, Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, Ludwig Schmidt, 2021 Communications of the ACM, Vol. 64 (Association for Computing Machinery) DOI: 10.1145/3472066 - This survey article offers a comprehensive overview of approximate nearest neighbor (ANN) search algorithms, which are crucial for efficiently scaling similarity search in large-scale vector databases.
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Patrick Lewis, Ethan Perez, Aleksa Gordić, Vladimir Karpukhin, Katja Filippova, Marc'Aurelio Ranzato, Mihai S. Rădulescu, Mike Lewis, Yann N. Dauphin, and Douwe Kiela, 2020 Advances in Neural Information Processing Systems (NeurIPS) (NeurIPS Foundation) DOI: 10.5591/neurips.2020.00949 - The foundational paper introducing the Retrieval-Augmented Generation (RAG) framework, detailing how the retrieval component, which relies on similarity search, is integrated with large language models for enhanced performance.
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