Turning AI Ideas into Working Prototypes
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Every big innovation starts with an idea. But ideas alone aren’t enough—they need direction, validation, and a solid plan to turn them into something impactful.
In this session, you’ll hear directly from the team at Drata on how they started their AI journey, focusing on choosing the right tools and strategies to quickly validate and bring their ideas to life.
In this session, we’ll geek out on:
Identifying high-impact AI opportunities for business value
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Crafting a robust proof of concept (PoC)
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Testing feasibility and linking AI to key business metrics
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Crafting a winning pitch for exec buy-in
Building Trust in Your AI System
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Now that you’ve built the v1 of your AI system, it’s time to evaluate how reliably it can run — across many test cases. Session II will focus on the strategies, metrics and goals you should set to prepare your system for production.
Join us for an in-depth look at how Redfin successfully developed their AI-powered virtual assistant, Ask Redfin, using a test-driven development approach.
In this session, we’ll geek out on:
The nuts and bolts of a test-driven development approach
Defining success: outcomes, cost efficiency, and latency.
Combining data-driven measures and LLM as a judge
Pro tips for hitting the mark with your LLM apps
Prod Time: Launch Your AI Smoothly
Building upon the foundations laid in Sessions I and II, this final session will equip you with the strategies and framework needed to seamlessly transition your LLM applications from prototype to production.
By the end of this session, you'll have a comprehensive understanding of the end-to-end process of bringing an AI application to production, reliably.
In this session, we’ll geek out on:
Maintaining quality in prod for consistent AI performance
The version control system you need for smooth iterations
Tips for capturing user feedback in prod
Post-prod evaluation strategies
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