Introduction

<aside> 💡

Key Learnings :

• LLM isn’t the product. The system around it is! Focus efforts to ensure the system is providing value to your end user.
• Good “Evals” is what differentiates a good AI Product team from a bad one! </aside>

With LLMs, building demos has become easier but building systems that reliably solve user tasks has become even harder considering LLMs are non-deterministic!

I learned this the hard way while building AI product (A developer copilot) which uses AI/LLMs extensively for

• Auto-migrating apps to new platform versions,
• Fixing errors & generating code for platform capabilities
• Helping developers go from idea to working app faster!

https://www.youtube.com/watch?v=fCjdLPdzEnw

https://www.youtube.com/watch?v=MdF_5-DTjXo

End users don't care about our fancy AI architecture, they care about getting their job done, consistently and reliably and with non-deterministic LLMs, that's harder than it sounds. That's why we need a solid AI evaluation framework.

As a Product Manager, building AI products, I will be writing extensively about AI Evals. This post is the first in a series in which I talk about AI evaluation frameworks - or "evals" as we call them in product teams.

Why invest in a robust AI evaluation framework

Here is why Evals matter :

• Measure if the product actually helps users complete their tasks
• Ensure users are getting predictable outcomes from the product
• Improve with confident by changing any component of the system (newer LLMs, better prompt engineering, etc)
• If something goes wrong, helps you debug and isolate the root cause!