Context Engineering for LLMs Guide

This comprehensive guide delves into Context Engineering for AI systems, providing essential techniques to design, structure, and optimize context for Large Language Models. Explore methods like context reduction, compression, chunking, and multi-source pipelines, alongside real-world examples and trade-offs. Learn to significantly improve AI output quality and efficiency in production environments.

Chapters

1. 01 Making Every Token Count: Context Reduction & Summarization 20m
2. 02 Optimize LLM Agent Context with Prioritization & Sliding Windows 18m
3. 03 Structuring Information for LLMs: Effective Context Design 17m
4. 04 The Core of LLM Intelligence: What is Context Engineering? 11m
5. 05 Beyond the Prompt: Building Multi-Source Context Pipelines (RAG) 15m
6. 06 Production-Ready Context: Best Practices & LLMOps 13m
7. 07 Breaking Down Information: Smart Chunking Strategies 14m
8. 08 Navigating the LLM's Memory: Understanding the Context Window 13m