On writing LLM evals in pytest
Pytest is the easy part. Evals are the hard part.
Hello fellow datanistas!
I’ve wondered how to systematically evaluate large language models (LLMs) beyond the usual 'vibe check'? So, in my latest blog post, I did an experiment: could we write structured evaluations for LLMs within the pytest framework, a popular framework in software testing?
In the post, I share my journey and the insights gained from setting up these evaluations, including the challenges of defining clear, actionable criteria and the nuances of implementing these tests in a real-world scenario. Whether you're a data scientist, a developer, or just curious about the inner workings of machine learning models, I hope you'll find inspiration in your approach to LLM testing.
Check out the post here!
If you find the insights useful, please forward this post to others who might also benefit from understanding more about structured LLM evaluations. Let's spread the knowledge and help each other build more reliable and effective machine learning systems!
Cheers,
Eric