Why Writing Good AI Evaluations is So Damn Hard and So Damn Essential

Many tech leaders consistently stress the importance of rigorous evaluations, acknowledging that creating powerful AI models is only half the battle. Without practical assessment, we risk deploying systems that are unpredictable, biased, or even harmful. Thought leaders across the AI community repeatedly highlight that the strength and safety of any AI system is fundamentally tied to the quality of the evaluations we build around it.

Good AI evaluations aren’t just nice-to-have; they’re essential for building trustworthy, safe, and effective AI.

Why are AI evals so difficult?

AI evals might sound straightforward; measure the system and report results, but in practice, they’re notoriously complex to design and implement well.

One common challenge is that evaluations often fall into a tricky gap: they’re either created by developers who deeply understand the technology but may lack complete insight into the business context or product nuances, or they’re managed by project managers who grasp the product goals but don’t fully understand the technical complexity behind creating robust evals. This disconnect frequently results in evaluations that either miss key business goals or overlook critical technical considerations.

Another significant complexity stems from the sheer variability of AI use cases. Consider the difference between an AI analyzing legal contracts and one powering a customer service conversational assistant. The evaluations for these two systems differ dramatically, both in technical implementation (how exactly you measure accuracy or usefulness) and from a business perspective (what defines success for the user or the organization). The uniqueness of each scenario makes developing reusable, high-quality evals especially challenging.

What can you do about it?

Recognizing the complexity and importance of AI evaluations is the first step; taking deliberate action is next. Here’s what you can do to improve your AI evals:

• Don’t treat evals as an afterthought. Integrate evaluation design into your AI development process from day one to ensure alignment between technical performance and business goals.

• Just as your AI system evolves, so should your evaluations. Regularly revisit and refine your evals (criteria, metrics, methodologies, etc.) to reflect changing requirements and insights.

• Involve a mix of technical experts, product managers, and business stakeholders in eval design. Diverse perspectives help reveal blind spots, uncover biases, and create more holistic, effective evaluations.

By following these steps, you’ll develop more effective evaluations that not only measure AI performance accurately but also ensure your AI systems truly deliver business value.