World's Fair 2025
[Evals Workshop] Mastering AI Evaluation: From Playground to Production
Overview
This talk focuses on mastering AI evaluation, moving from initial development to production. It emphasizes that even the best large language models (LLMs) require a robust testing framework due to issues like hallucinations and performance degradation with changes. Effective evaluation helps answer critical questions about model selection, cost-effectiveness, brand consistency, and ongoing improvement, ultimately reducing development time, costs, and enabling faster iteration.
Who should watch
- AI Engineers
- Product Managers
- Builders working with AI systems
- Anyone looking to improve the quality and reliability of AI features
- Teams struggling with debugging and troubleshooting AI applications
- Those aiming to scale AI development teams and processes
Key takeaways
- Evals are structured tests designed to measure the quality, reliability, and correctness of AI systems across various scenarios.
- The core components of an eval are the task (the code or prompt being evaluated), the dataset (real-world examples or test cases), and the score (the logic for grading the output).
- Offline evals are performed during development for proactive issue identification, while online evals monitor production traffic in real-time for diagnosis and feedback.
- A matrix is presented to help determine whether to improve evals or the AI application itself, based on the perceived output quality versus the eval score.
- Brain Trust offers both a UI for quick iteration and an SDK for version-controlled prompt management and automated evals.
- Logging production traffic is crucial for observability, debugging, and transforming live interactions into datasets for continuous improvement.
- Human-in-the-loop processes, including manual review and direct user feedback (like thumbs up/down), are vital for establishing ground truth, validating LLM judges, and ensuring AI products meet user expectations.
- Starting with minimal datasets and scores and iterating is recommended over striving for perfection upfront.
Notable quotes
*The best LLMs don't always guarantee consistent performance. So this is why you need to have a testing framework in place.*
*Evals help you answer questions: What type of model should I use? What's the best cost for my use case? What's going to perform best in all of the edge cases?*
*Playground you could think of as quick iteration; experiment. So a playground ephemeral; experiments long lived historical analysis.*
Unofficial community note. Prefer the recording for nuance.