Deployed: The AI Product Podcast
Building High-Performance AI Engineering Teams with Mike Conover, Co-founder & CEO of Brightwave
Sep 17, 2024
Mike Conover, co-founder and CEO of Brightwave, dives into the challenges and capabilities of AI in financial research. He discusses limitations of large language models (LLMs) and the importance of effective information retrieval. Mike shares insights on building strong AI engineering teams and the significance of practical collaboration between analysts and engineers. He emphasizes the need for customized AI solutions to enhance product outcomes, illustrating how Brightwave revolutionizes market analysis.
37:34
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Quick takeaways
- Effective AI systems necessitate reliable measurement and continuous assessment to ensure quality and accuracy in financial research outputs.
- Decomposing complex problems into manageable sub-tasks significantly enhances analysis clarity and output quality when leveraging Large Language Models.
Deep dives
Operationalizing Measurement for AI
To effectively operationalize artificial intelligence systems, it's essential to define a reliable set of measurements and continuously assess their accuracy. Unreliable measurement instruments can lead to significant issues in quality and efficacy. Iterative improvements can be made by observing the outcomes within a controlled environment, such as a playground or production setting. This tightening of the system's parameters, akin to a ratchet, gradually eliminates undesired outcomes, ensuring a more precise function.
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