Context Engineering for Engineers

Jeff Huber, founder of Chroma, shares why building with large language models isn’t just about prompts or RAG—it’s about context. He explains how deciding what goes into the context window shapes reliability, why performance drops with long inputs, and how careful filtering and compaction can make AI systems faster and more useful. Chapters: 00:00 - Introduction to Context Engineering 00:26 - Understanding AI Systems as Programs 01:29 - The Concept of Context Engineering 02:02 - Building Reliable Software with AI 02:31 - Challenges with Long Contexts 03:07 - Chroma's Technical Report Insights 03:57 - Needle in a Haystack Problem 05:08 - The Importance of Context in AI Tasks 06:05 - Gather and Glean Model 06:44 - Data Gathering Techniques 07:31 - Gleaning and Optimizing Data 08:26 - Content Engineering for Agents 09:35 - Challenges with Agent Performance 10:13 - The Role of Compaction 10:57 - Conclusion and Final Thoughts