Ep 25. Accelerate Your GenAI Project with MVPs & Evaluation Frameworks

Welcome to Episode 25 in Prolego’s Generative AI series! This is the third in our Step-by-Step mini-series where I show you how to build your first generative AI product from start to finish.  In this episode, I take you on a journey to efficiently launch your generative AI product, focusing on MVP (Minimum Viable Product) release and crafting an evaluation framework.

What’s Inside This Episode?

  • MVP Release Insights: Inspired by Steve Blank's, "Four Steps to the Epiphany", I delve into the true essence of an MVP in generative AI. Discover why it’s crucial to release your MVP as soon as it resolves basic issues, rather than waiting for a complex, full-featured product.
  • Practical Guidelines: I offer actionable advice, like starting with a single workflow problem and making one LLM call per user action. This approach not only speeds up your system but also enables rapid enhancements based on user feedback.
  • Evaluation Framework Strategy: Post-launch, the focus shifts to establishing a robust evaluation framework. Learn how even minor changes can impact your application and explore our two-fold strategy for output analysis: script-based checks and LLM-assisted evaluations.
  • Ground Crew Case Study: Get a sneak peek into Prolego’s ongoing project, Ground Crew. I’ll show you how we’re implementing an evaluation framework to enhance functionalities like code maintenance and knowledge management.

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