Welcome to the community!

Setting up rigorous, reliable LLM evaluations is essential to taking AI systems from prototype to production. Whether you're working with custom unit test suites, LLM-as-a-judge patterns, or domain-specific benchmarks, we want to hear how you're approaching it.

Quick Discussion Questions:

• What models and metrics are you currently evaluating?

• What bottlenecks or edge cases have given your evaluation pipelines the most trouble?

Drop your thoughts, current setup, or any questions below!

Welcome to the community! Whether you are building production systems or getting started with evaluations, having a structured benchmark workflow is critical. Question for everyone: when setting up your evaluation pipeline, what is your biggest challenge with which LLM evaluation metrics to test first? Drop your thoughts or questions below!

Full-Stack AI Engineering Course (FastAPI, LLMs, Docker, Supabase)

https://whop.com/gillanidigitalhub/full-stack-ai-engineering-course-fastapi-llms-docker-supabase-aa?a=mistyflash20

‎How to Systematically Setup LLM Evals (Metrics, Unit Tests, LLM-as-a-Judge)

‎https://whop.com/gillanidigitalhub/how-to-systematically-setup-llm-evals-metrics-unit-tests-llm-as-a-judge-6d