// ARTIFICIAL INTELLIGENCE

This chapter introduces JSON and TOON, explaining their role in AI, why they are critical for Large Language Models, and guides you through setting up …

Discover what Agentic Lightening is, why it's a game-changer for AI agent development, its key benefits, and a brief history. Learn how this Microsoft …

This chapter delves into the fundamental components of JSON: objects, arrays, and primitive data types. Learn their syntax, structure, and practical …

Dive into the fundamental building blocks of Agentic Lightening. Understand the roles of `LitAgent`, `Trainer`, and the `AgentLightningServer`, and …

This chapter introduces TOON (Token-Oriented Object Notation), explaining its core syntax, design philosophy for token efficiency, and how it differs …

Learn how to seamlessly integrate your existing AI agents, built with frameworks like LangChain, OpenAI Agent SDK, AutoGen, and CrewAI, into Agentic …

This chapter introduces JSON Schema for defining, validating, and documenting the structure of JSON data. Learn how to create schemas, enforce data …

Delve into the critical concepts of 'rollouts' and 'rewards' in Agentic Lightening. Learn how agents execute tasks, how their interactions are …

Explore TOON's advanced syntax, including key folding, custom delimiters, and detailed quoting rules. Learn best practices for maximizing token …

Explore the advanced optimization algorithms supported by Agentic Lightening, including Reinforcement Learning (RL), Automatic Prompt Optimization …

This chapter provides a deep dive into the performance characteristics of JSON and TOON. It covers token cost analysis, benchmarks across various LLMs …

This chapter delves into hybrid data strategies, integrating JSON and TOON in AI workflows. It covers the tools, libraries, and best practices for …

Build a basic Question-Answering (QA) agent and leverage Agentic Lightening's capabilities for Automatic Prompt Optimization (APO) to enhance its …

Take your LangChain agent to the next level by integrating it with Agentic Lightening for Reinforcement Learning (RL) based optimization. This project …

A curated collection of resources to continue your journey with Agentic Lightening and the broader field of AI agents. Includes recommended courses, …

A comprehensive guide to JSON and TOON, two essential data formats for AI and Large Language Models. Learn their fundamentals, advanced usage, …

A comprehensive guide to Microsoft's Agentic Lightening framework, designed for absolute beginners and experienced developers to train and optimize AI …

A comprehensive, beginner-friendly guide to the Microsoft Agent Framework, covering core concepts, advanced topics, and practical projects for …

A comprehensive, beginner-friendly guide to building intelligent AI agents in Java using Spring Boot, covering foundational concepts, practical …

A comprehensive, beginner-friendly textbook-style document on Model Context Protocol (MCP), focusing on creating MCP servers for AI agents and …

An in-depth exploration of advanced concepts, robust architectures, and real-world complex applications of agentic AI for experienced professionals, …

A comprehensive guide to advanced Python programming, covering clean code principles, performance optimization, concurrency with asyncio, and best …

A comprehensive guide to building intelligent AI agents using LangChain and LangGraph, covering foundational concepts, tool integration, memory …

A comprehensive, beginner-friendly guide to understanding, building, and applying agentic AI for UI and backend systems using popular libraries like …

A comprehensive guide to understanding Large Language Model (LLM) architectures, from fundamental types and parameter counts to the specific …

A comprehensive guide to Large Language Model (LLM) quantization, covering its principles, various techniques (4-bit, 8-bit, GGUF), practical …

A comprehensive guide to deploying and serving Large Language Models (LLMs) locally, focusing on Ollama for running pre-packaged models, and …

A comprehensive guide to Deep Learning with PyTorch, covering foundational tensor operations, neural network construction, custom training, and …

A comprehensive guide to Large Language Model (LLM) pre-training and fine-tuning concepts, covering Supervised Fine-tuning (SFT), Parameter-Efficient …

A comprehensive guide to traditional Machine Learning concepts and practical application with Scikit-learn, from basic regression and classification …

A comprehensive and practical guide to MLOps and LLMOps principles and practices for managing the lifecycle of Large Language Models and Agentic AI …

A comprehensive guide to Natural Language Processing fundamentals, including text preprocessing, word embeddings, and an in-depth exploration of …

A comprehensive and practical guide to Retrieval-Augmented Generation (RAG), covering its core components, document loading, chunking, embedding, …