Hamel Husain Use Cases
1
ML engineers looking for practical strategies to evaluate and debug LLMs (error analysis, adversarial validation, eval harnesses).
2
Product managers and technical leads designing reliable AI features who need frameworks and checklists for model evaluation and deployment trade-offs.
3
Data scientists seeking reproducible workflows, tooling recommendations, and examples for synthetic data creation, tokenization pitfalls, and production notebooks.
4
Teams evaluating open-source libraries and patterns for running LLM evals or integrating inspection tools into their CI and monitoring pipelines.
5
Developers and learners who want to take the AI Evals course or hire consulting to operationalize evaluation systems and improve product quality.