Getting Started with Kerb Toolkit
Chapter 1: Introduction and First Text Generation
Course Overview and Setup
Principles of the Toolkit
Configuring LLM Providers
Executing Your First Generation Call
Handling Streaming Responses
Chapter 2: Advanced Prompting Techniques
Introduction to Prompt Engineering
Creating Dynamic Prompts with the Template Engine
Managing and Versioning Prompts
Implementing Few-Shot Prompting
Extracting Structured Data from LLM Outputs
Parsing JSON and Code Snippets
Chapter 3: Managing Context and Tokens
The Importance of the Context Window
Counting Tokens with the Tokenizer
Strategies for Text Truncation
Managing Token Budgets for Complex Prompts
Chapter 4: Preparing Data for Retrieval
Data Loading Fundamentals
Loading Documents from Different Sources
The Rationale Behind Text Chunking
Applying Chunking Strategies
Text Preprocessing for Better Retrieval
Chapter 5: Embeddings and Semantic Search
Understanding Text Embeddings
Generating Embeddings
Fundamentals of Vector Similarity
Performing Semantic Search
Choosing an Embedding Model
Chapter 6: Building Retrieval-Augmented Generation (RAG) Systems
Anatomy of a RAG System
Creating a Simple Retrieval Pipeline
Implementing Different Search Methods
Improving Relevance with Re-ranking
Managing Retrieved Context for Generation
Chapter 7: Building Conversational Applications with Memory
The Challenge of Stateful Conversations
Implementing Conversation Buffer Memory
Using Summary Memory for Long Conversations
Tracking Entities Across a Conversation
Chapter 8: Developing Autonomous Agents
Introduction to LLM Agents
The ReAct Pattern for Reasoning and Acting
Building a ReAct Agent
Defining and Using Tools
Implementing Plan-and-Execute Agents
Orchestrating Multi-Agent Systems
Chapter 9: Optimizing for Performance and Cost
Identifying Performance Bottlenecks
Implementing LLM Response Caching
Caching Embeddings to Reduce API Calls
Cache Invalidation Strategies
Chapter 10: Ensuring Application Safety and Reliability
Adding Safety Guardrails to Applications
Implementing Content Moderation
Detecting and Masking Personal Information
Introduction to Testing LLM Applications
Using Mocks for Deterministic Tests
Chapter 11: Advanced Capabilities
Processing Image Inputs with Multimodal Models
Benchmarking LLM Outputs with Evaluation Metrics
Preparing Datasets for Fine-Tuning
The Rationale Behind Text Chunking
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- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Patrick S. H. 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, Vol. 33 (Advances in Neural Information Processing Systems) DOI: 10.48550/arXiv.2005.11401 - Introduces Retrieval-Augmented Generation (RAG) and outlines its fundamental architecture, including the preparation of knowledge sources.
- Understanding tokens and context windows, OpenAI, 2025 (OpenAI) - Provides a clear explanation of how tokens work, the concept of context windows, and their importance in Large Language Models.
- A Survey on Retrieval-Augmented Generation, Yunfan Shao, Zhicheng Dou, Jiantao Ji, Xiaoxue Li, et al., 2024 arXiv preprint arXiv:2401.12192 (arXiv) DOI: 10.48550/arXiv.2401.12192 - A recent survey offering a comprehensive review of Retrieval-Augmented Generation, with sections dedicated to data preparation techniques like text chunking.
- Text Splitters Overview, LangChain (LangChain) - Describes various text splitting strategies used in RAG systems, illustrating methods beyond simple fixed-size chunks to preserve context.