AI Evals Strategy & Implementation Consulting

This session is designed for AI product leaders, data scientists, and engineers who are building or improving an AI evaluation program.

It may be especially helpful if you are:

• Starting an AI eval practice or team

• Trying to align product, engineering, data science, and leadership

• Designing an evaluation strategy for an AI product

• Selecting metrics, datasets, tools, or frameworks

• Figuring out how to move from vibe checking to a structured evaluation process that guides AI development.

Bring your most important AI eval questions. Depending on your needs, we can discuss:

• AI eval strategy and roadmap

• Team structure, roles, and resourcing

• Stakeholder alignment and executive communication

• Evaluation criteria, metrics, and rubrics

• Test-set and dataset development

• LLM-as-a-judge and human evaluation

• RAG, agent, and multi-turn evaluation

• Eval tooling and implementation architecture

• Production monitoring and continuous evaluation

• Diagnosing failures and prioritizing improvements

By the end of the session, you will have:

• Clearer direction on your highest-priority eval challenges

• Practical recommendations tailored to your product and organization

• Actionable next steps for your team

• Relevant frameworks, examples, or resources to support implementation

This is a working session, not a generic presentation. We will focus on your specific questions and context.