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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