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Really cool! Awesome launch! Question - does Giskard also makes recommendations on how to avoid hallucinations? Detecting hallucinations has been working ok for us but the real problem is figuring out how to prevent them moving forward. Wonder if you have any thoughts?
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@nikhil_kulkarni thanks for the comment! Mitigation is on our roadmap - we have ML researchers working on this. It's an open problem and we're really looking forward to integrating it in our product suite!
This is probably one of the most exciting products in the MLOPs space. Ensuring the production quality of your models and reduce your technical debt is for sure a needed step but mostly not implemented by ML teams around the world. Of course, testing an LLM model is definitely a daunting task on top of the pile of work needed to train and deploy your model and Giskard makes it for you!
co-author of CML
In our company, we face a serious problem when creating our internal LLMs for data extraction. Our biggest problems are hallucinations and model biases.
With Giskard we can finally monitor our models automatically instead of doing a lot of manual work and running several hand-crafted non-inclusive tests
Highly advise you to check Giskard out!
I was wondering since last few days about why there isn't any framework for LLM product testing. I am telling right now to my testing and QA team to review and start using.
I hope you guys have support in place to answer any questions they might have during the adoption process. Good Luck!!
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