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
Implementing the Retriever
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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- 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 33 (NeurIPS 2020), Vol. 33 (Curran Associates, Inc.) DOI: 10.48550/arXiv.2005.11401 - Defines the original Retrieval-Augmented Generation (RAG) framework, detailing its architecture and benefits for incorporating external knowledge into language models.
- 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) DOI: 10.18653/v1/D19-1410 - Introduces the Sentence-BERT model, which efficiently generates high-quality sentence embeddings, forming the basis for libraries like
sentence-transformersused in the retriever. - ChromaDB Documentation, Chroma, 2024 - Official guide for Chroma, an open-source embedding database. It provides details on installation, client usage, collection management, and querying, relevant to implementing the retriever.
- Sentence-Transformers Documentation, Nils Reimers, Iryna Gurevych, 2024 - Official guide for the
sentence-transformersPython library, detailing how to load pre-trained models and generate sentence embeddings, crucial for converting queries into vectors.
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