With frequent updates to our chatbot, we often face unexpected issues—like changes in tone or functionality that lead to confusing interactions. Currently, our regression testing consists of a handful of people manually chatting with the bot, which feels subjective and doesn't scale. I'm curious to know how other teams are handling this. Are you treating AI agents like traditional software, or is everyone just figuring it out as they go?
3 Answers
If you want an efficient testing method, customer emulation could be a route. Run your AI through various scenarios that test its limits, like asking it to handle sensitive topics. This way, you can gauge whether it maintains performance standards without needing a ton of manual oversight.
One idea is to let your AI chat with itself and analyze the conversation. This might highlight inconsistencies and issues that manual testing misses. While it won't catch everything, it could surface some key problems before they reach users.
Non-deterministic systems like AI do pose a challenge! It’s tough to provide full coverage for every potential input. Consider using scripted interactions that minimize the chance of the agent going off track. Just ensure your scripts are tight enough to catch failures without leaving room for miscommunication.
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