Code search, Copilot, LLM prompting with empathy and Artifacts with John Berryman
From Vector Podcast by Dmitry Kan
February 10, 2025 · 1h 7m · Season 3 · Episode 9
About this episode
John Berryman discusses his expertise in AI application development and search technology, including his work on GitHub Copilot and prompt engineering.
Vector Podcast website: https://vectorpodcast.com Get your copy of John's new book "Prompt Engineering for LLMs: The Art and Science of Building Large Language Model–Based Applications": https://amzn.to/4fMj2Ef John Berryman is the founder and principal consultant of Arcturus Labs , where he specializes in AI application development (Agency and RAG). As an early engineer on GitHub Copilot, John contributed to the development of its completions and chat functionalities, working at the forefront of AI-assisted coding tools. John is coauthor of "Prompt Engineering for LLMs" (O'Reilly) .Before his work on Copilot, John's focus was search technology. His diverse experience includes helping to develop next-generation search system for the US Patent Office, building search and recommendations for Eventbrite, and contributing to GitHub's code search infrastructure. John is also coauthor of "Relevant Search" (Manning) , a book that distills his expertise in the field.John's unique background, spanning both cutting-edge AI applications and foundational search technologies, positions him at the forefront of innovation in LLM applications and information retrieval. 00:00 Intro 02:19 John's…
People in this episode
Host: Dmitry Kan
Guest: John Berryman
Topics covered
- AI application development
- GitHub Copilot
- prompt engineering
- search technology
- large language models
- information retrieval
Keywords
- AI
- GitHub Copilot
- prompt engineering
- search technology
- large language models
- information retrieval
Mentioned in this episode
Organizations: Arcturus Labs, GitHub, US Patent Office, Eventbrite
Products: Prompt Engineering for LLMs: The Art and Science of Building Large Language Model–Based Applications, Relevant Search
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