Introduction to AI Evals Hey, Bala again. In this session, Gautham Muthukumar from Microsoft takes us through AI evals.
Key Takeaways
- Evals are critical for assessing AI model/agent performance and continuously improving them
- Key eval types: benchmarks, programmatic evaluations, system performance metrics, human-in-the-loop, and LLM as judge
- Tools like Langfuse provide observability and eval capabilities out-of-the-box
- Evals should be precise yet continuously updated as models and use cases evolve
Topics
- Importance and Complexity of Evals
- Evals are crucial for determining AI product success, considered "the real moat" by industry leaders
- Evals cover general evaluations, observability metrics, and responsible AI aspects
- Agent evals are more complex than traditional software/ML testing due to non-deterministic, multi-path nature
Types of Evals
- Benchmarks: Standard datasets (e.g. MMLU, GPQA) or custom synthetic data with golden answers
- Programmatic evaluations: Simple automated checks (e.g. SQL syntax, summary length)
- System performance: Latency, cost, error rates
- Human-in-the-loop: Expert review of agent outputs
- LLM as judge: Using another LLM to evaluate outputs based on rubrics
Eval Tools Demo (Langfuse)
- Provides observability metrics, trace visualization, and eval capabilities
- Allows creating datasets, experiments, human annotations, and LLM judges
- Integrates with notebooks/code via API keys and tracing libraries
Best Practices
- Combine offline and online evals
- Be precise in metrics, but expect to evolve evals over time
- Consider implicit user feedback (e.g. follow-up questions) in addition to explicit ratings
- For subjective answers, use majority customer opinions and personalization
Next Steps
- Explore Hugging Face agents course and Weights & Biases apps evaluation course
- Review shared YouTube video on setting up evals
- Investigate Langfuse documentation for more advanced usage (e.g. tagging)
- Continue evolving evals as models and use cases develop
Here's the entire recording of the session.