← All speakers

Bio, Work & Ideas

Jeff Huber

Conference affiliation: Chroma · 2025

Jeff Huber is the co-founder and chief executive of Chroma, the company behind ChromaDB, an open-source database and search infrastructure for AI applications. He builds systems that help models retrieve useful information, evaluate whether that information actually answers users’ questions, and preserve organizational knowledge for AI agents.

Huber studied economics and industrial engineering at North Carolina State University before leaving to launch a startup. He worked in software engineering and product leadership and co-founded Standard Cyborg, a computer-vision company, before founding Chroma with Anton Troynikov. Chroma raised an $18 million seed round in 2023 and expanded from a locally installable vector database into cloud infrastructure and shared knowledge systems.

  • Application-specific retrieval evaluation. Huber measures retrieval against matched user queries and relevant documents, using metrics such as recall@10 to compare embedding models and chunking strategies.
  • Context engineering. Huber describes preparing model inputs as gathering potentially relevant information and gleaning away distractions. His framework for context selection combines retrieval, filtering, ranking, and application-specific evaluation; his co-authored research on context rot shows why longer inputs can degrade model performance even when models advertise expansive context windows.
  • Organizational memory for agents. Huber argues that companies should own their operational context, retain institutional knowledge, and learn from production traces. Chroma’s Foundation extends this approach into a shared knowledge layer for agents and teams. He has also highlighted copy-on-write collection forking as infrastructure for experimentation, development sandboxes, versioned deployments, and read replicas.

Read the topics behind these talks

1 conference talk

Key ideas

Scroll to read ↓

Fast retrieval tests show which changes help your application, while structured conversation analysis reveals where better tools, filters, and workflows will matter.

  • Which documents should retrieval return?
    0:43 ↗
  • Generate questions that resemble real queries
    3:06 ↗
  • Test an embedding change on your application
    4:29 ↗
  • Read the feedback already inside conversations
    7:10 ↗
  • Give an aggregate score some context
    9:12 ↗
  • Extract records you can analyze
    10:08 ↗
  • Use Kura to build a hierarchy of needs
    11:19 ↗
  • Build the capability a segment is missing
    13:08 ↗
  • Prioritize using both usage and performance
    14:10 ↗
  • Monitor categories and test clearer hypotheses
    15:18 ↗
  • Justify an investment by the users it could help
    17:04 ↗
  • Q&A: price the work an agent completes
    18:49 ↗

References