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I really enjoyed this episode with Gideon Mendels from Comet. Recently LLM evaluation has been a topic I've been reading a lot about and incorporating into my work as I use LLMs more and more in my daily job.
My main takeaway from the episode was that evaluating LLM outputs is not terribly unlike evaluating traditional machine learning models. In both you undertake rigorous experimentation during development. In traditional ML, you're tweaking hyperparameters, adjusting datasets, and fine-tuning model architectures while tracking performance metrics. With LLMs, you have hyperparameters to tune - prompt engineering, retrieval settings and embedding models etc. - but the systematic approach remains the same. You're still iterating through experiments, measuring outcomes, and making data-driven decisions.
Gideon highlighted how the most successful teams are those who've recognized this parallel and adapted their ML evaluation practices to LLM development. They maintain test datasets, track experiments meticulously, and integrate evaluation into their CI/CD pipelines. Even though LLMs introduce new challenges with their non-deterministic outputs, the fundamental process of systematic experimentation and measurement hasn't changed.
Having systematic LLM evaluations has numerous benefits. First, AI feature development can be done more confidently. Maintaining robust evaluation pipelines allows teams to iterate on new features quickly while maintaining quality. Second, evaluation can mitigate risk from the LLM of making costly mistakes - incorrect outputs, biased responses, disclosure of sensitive information. These issues can be caught and handled during development rather than a customer exposure. Finally, another benefit is scalability. Tracking performance in production can help developers prioritize areas for improvements. Embedding evaluation metrics into a CI/CD pipeline allows teams to confidently push updates without fear of regression.
There are certainly LLM-specific challenges for evaluations. Scaling evaluations one of those challenges.. Running a LLM judge with an advanced model for all your production data gets expensive both finanically and computationally. Adding flexibility to handle non-deterministic outputs from LLMs is also a challenge. Deterministic tests and string matching are brittle and prone to failure if the LLM expresses the same response with slightly different wording.
As the use of LLMs matures, we'll likely see more standardization around evaluation practices, better solutions for scaling evaluations, and more sophisticated approaches to handling non-deterministic outputs. While LLMs introduce new complexities, the fundamental principles of systematic experimentation and evaluation remain crucial for building reliable AI applications. Teams that embrace this mindset and invest in robust evaluation practices will be better positioned to deliver value with their LLM applications.
The MLOps Community podcast is a new discovery to me. I really enjoyed this first episode and there are a number of episodes in the backlog I marked to listen to later. Looking more to sharing more of my favorites!