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Part VI: Assessment & Reference
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Contents
Disclaimer
About the Author
Preface
Introduction
Part I: Foundations
LLM Architecture and Optimization Methods
Systems Foundations for LLMs
Introduction to Reinforcement Learning
Part II: RL Methods for LLMs
RL Foundations for Language Models
PPO — Proximal Policy Optimization
DPO — Direct Preference Optimization
GRPO — Group Relative Policy Optimization
Preference Optimization Variants
Reward Model Training
SFT Best Practices and Techniques
System Architecture & Infrastructure at Scale
LLM Agentic Training
Part III: Reasoning
RL for Large Reasoning Models
Part IV: Evaluation
LLM Evaluation
Part V: Agentic AI
Introduction to Agentic AI
Retrieval-Augmented Generation (RAG)
Agentic Memory Systems
Agent Harness – Context Management and Orchestration
Agent Design Patterns
Agentic Environments and Benchmarks
Model Context Protocol (MCP)
Agent Skills
Agent-to-Agent Communication (A2A)
Multi-Agent Systems
Agent Development Frameworks
Agentic UI Frameworks
Part VI: Assessment & Reference
Quiz Questions & Detailed Answers
Quick Reference
Conclusion and Future Directions
Part VI: Assessment & Reference
Chapter 37 of 40 · Haggai Roitman
Part VI: Assessment & Reference
Part VI
Assessment & Reference
Chapter 27
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