I had a great discussion with Dhruv Singh, CTO & Co-Founder of HoneyHive AI, about the best ways to evaluate GenAI systems. We started with the basics but got into the real details when we discussed evaluating complex AI Agents. Evaluations are key for GenAI tech because if you can’t measure how well your AI is working, things will quickly go off track as systems get more complicated. I’d be happy to answer any questions you have about this topic or the conversation itself!
The deep dive into the role of evaluations in AI systems offers a strong foundation for ensuring AI agents are reliable and effective. One point that could take this further is the idea of adding adaptive eval systems that adjust based on how the AI is performing in real time. If you could get continuous feedback and adjust immediately, AI agents could respond much better to changes and problems.
Another important point is modular evals. This would allow independent checks on different parts of the system, helping improve specific areas without the need to evaluate everything all at once. This method would save time and resources.
As AI agents become more independent, we also need to consider the ethical side of evaluations. It’s not just about accuracy, but also fairness, transparency, and accountability. Making sure AI evaluations respect these values will help gain trust from the public.
Finally, it would be great to see the AI community work together with experts from other fields, like psychology or ethics, to build better eval systems. This way, AI evaluations can be more well-rounded and not just based on technical metrics.