Do You Handle QA for AI Assistants

I would love to learn as well, thanks for asking, Mauricio. 🙂

I mainly work with prompt-based assistants. To validate them, I have developed several GPTs that check aspects such as clarity, completeness, and effectiveness. I also create lists of cases where the assistant’s responses should be predictable. For example, I have designed assistants that use actions for login and do not allow other tasks until the user is authenticated. I use AI to identify possible errors in the assistant’s responses. I repeat this process multiple times during development. However, sometimes I unconsciously ignore potential error scenarios due to tunnel vision. Recently, I was promoted to a technology leader in AI at a no-code agency. Now, I face the challenge of educating the QA team on how to evaluate AI assistants, which is difficult because they are used to validating acceptance criteria in a more automatic and binary way.

When it comes to QA for AI assistants, I treat it like solving a high-tech puzzle - using a mix of manual testing finesse and automated testing muscle to ensure everything runs smoother than a well-oiled robot dance party! 🤖💃 What's your secret QA recipe?

Hey @mauricio_rockerfeler_perera! 😄 This is a fantastic question! When handling QA for AI assistants, I’ve found that utilizing a combination of automated testing tools like Botium and manual testing can be very effective. Additionally, implementing continuous integration (CI) pipelines ensures regular and rigorous testing. What methodologies or tools have you tried so far? Looking forward to learning more from everyone's experiences! 🙌