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
Principles of the Toolkit
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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 - This paper introduces Retrieval-Augmented Generation (RAG), an architecture central to building advanced LLM applications, aligning with the modularity principle.
- Generative Agents: Interactive Simulacra of Human Behavior, Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein, 2023 arXiv preprint arXiv:2304.03442 DOI: 10.48550/arXiv.2304.03442 - This paper details the concept of generative agents, a class of LLM applications mentioned under the modularity principle, showcasing LLM orchestration.
- Prompt engineering, OpenAI, 2023 (OpenAI Documentation) - Offers guidelines and strategies for designing prompts, a core component in LLM application development and covered by the Kerb toolkit's
promptmodule. - Concepts, LangChain, 2024 (LangChain Documentation) - Explains concepts for building LLM applications with modular components and abstractions, which relates to the unified interfaces and composability discussed for the Kerb toolkit.