MLOps.community How to Systematically Test and Evaluate Your LLMs Apps // Gideon Mendels // #269
91 snips
Oct 18, 2024 Gideon Mendels, CEO and co-founder of Comet, dives into the intricate world of testing and evaluating LLMs. He discusses the hybrid approach required for these applications, merging machine learning with software engineering best practices. Topics include innovative methods for evaluating LLMs beyond traditional metrics, the challenge of unit testing with deterministic assertions, and the importance of experiment tracking in ensuring reproducibility. Gideon also highlights the role of user interaction analysis in enhancing LLM applications' performance.
AI Snips
Chapters
Transcript
Episode notes
LLM App Evaluation
- Define comprehensive unit tests for LLM app use-cases.
- Use evaluation metrics like accuracy, F1 scores, perplexity, and LLM-as-a-judge.
LLM vs. Traditional ML Evaluation
- Traditional ML model evaluation focuses on the model during training.
- LLM evaluation focuses on the output after training, often using a hybrid approach.
Bridging Paradigms
- Software engineers building with LLMs should understand data science paradigms.
- Data scientists should adopt software engineering principles like Git and testing.