The Example Engine: How Exa Is Creating the AI Librarian for the Web with Will Bryk, CEO of Exa

In this episode, Nathan sits down with Will Bryk, CEO of Exa.ai. They discuss how Exa enables complex, research-based searches that differ from traditional keyword-based search, how their AI uses neural and non-neural methods, why they are using their own vector database, and more. Try the Brave search API for free for up to 2000 queries per month at https://brave.com/api

LINKS:

- Exa.ai: https://exa.ai/

X/SOCIAL:

@WilliamBryk (William)

@labenz (Nathan)

@ExaAILabs (Exa)

SPONSORS:

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The Brave search API can be used to assemble a data set to train your AI models and help with retrieval augmentation at the time of inference. All while remaining affordable with developer first pricing, integrating the Brave search API into your workflow translates to more ethical data sourcing and more human representative data sets. Try the Brave search API for free for up to 2000 queries per month at https://brave.com/api

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TIMESTAMPS

(00:00:00) - Preview

(00:01:52) - Exploring the landscape of information retrieval tools

(00:14:19) - Exa: AI librarian for the web

(00:15:32) - Sponsors: Oracle | Omneky

(00:16:57) - AI Project Ideas

(00:22:26) - What does search built for AI mean?

(00:25:40) - Ranking with neural and non-neural methods

(00:30:50) - Sponsors: Brave | On Deck

(00:33:13) - Best practices for Exa prompting

(00:34:01) - The power of keyword search

(00:37:26) - Breaking down complex queries

(00:34:37) - The evolution of search engines

(00:39:53) - The business side of Exa

(00:40:17) - The challenges of building a web scale index

(00:39:53) - The business side of Exa

(00:50:59) - Vector DBs

(00:58:17) - Wrap

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