weber.st.michael
New member
Hi everyone,
I’ve been transitioning our regression suites to include generative models lately, and the biggest bottleneck isn't the AI itself, but how we validate it. Standard testing logic just breaks when the output is non-deterministic.
I’m currently digging into llm testing (https://testomat.io/blog/llm-test/) strategies to see if we can automate the verification of model accuracy without constant manual oversight. It’s a steep learning curve, especially when trying to integrate this into a standard CI/CD pipeline.
On the other hand, the market is flooded with AI testing tools (https://testomat.io/tag/ai-testing/) promising 'zero-maintenance' and 'self-healing' scripts. In my experience, some are great for simple UI, but struggle with complex business logic.
Does anyone here have hands-on experience with these tools in a production environment? Which ones actually handle high-frequency deployments without becoming a maintenance nightmare?
Looking forward to your insights!— Michael
I’ve been transitioning our regression suites to include generative models lately, and the biggest bottleneck isn't the AI itself, but how we validate it. Standard testing logic just breaks when the output is non-deterministic.
I’m currently digging into llm testing (https://testomat.io/blog/llm-test/) strategies to see if we can automate the verification of model accuracy without constant manual oversight. It’s a steep learning curve, especially when trying to integrate this into a standard CI/CD pipeline.
On the other hand, the market is flooded with AI testing tools (https://testomat.io/tag/ai-testing/) promising 'zero-maintenance' and 'self-healing' scripts. In my experience, some are great for simple UI, but struggle with complex business logic.
Does anyone here have hands-on experience with these tools in a production environment? Which ones actually handle high-frequency deployments without becoming a maintenance nightmare?
Looking forward to your insights!— Michael