Master AI Evaluations: Essential Skills for Product Builders

Why Evals Matter

Building great AI products requires mastering evals. It’s the highest ROI activity you can engage in, and it’s surprisingly addictive. Once you start, you learn a lot and improve your product significantly. The goal isn’t perfection but actionable improvement. You don’t need to do evals repeatedly; often, a single thorough eval can set a strong foundation for your product.

Common Misconceptions About Evals

Many people think evals are just unit tests or that AI can handle them alone. But evals are about systematically measuring and improving AI applications. They require human insight, especially in the initial stages. Misunderstandings often arise from past experiences where evals were done poorly, leading to distrust. It’s crucial to approach evals with a clear understanding and proper methodology.

The Role of a Benevolent Dictator

In the eval process, appointing a ‘benevolent dictator’ can streamline decision-making. This person, ideally with domain expertise, oversees the eval process, ensuring it’s efficient and not bogged down by committee decisions. Often, this role falls to the product manager. The key is to trust their taste and expertise to make quick, informed decisions that improve the product.

“”To build great AI products, you need to be really good at building evals.””

Evals vs. AB Tests

There’s a debate about whether evals are necessary if you have AB tests. However, evals and AB tests serve different purposes. Evals focus on systematically measuring application quality, often before deployment, while AB tests measure the impact of changes on user behavior. Both are essential, but evals provide a deeper understanding of specific failure modes and product quality.

The Process of Error Analysis

Error analysis is the first step in building effective evals. It involves looking at data, identifying errors, and categorizing them. This process helps you understand what’s going wrong in your application. It’s crucial to write detailed notes and categorize errors into actionable failure modes. This structured approach leads to significant product improvements.

Using LLMs in Evals

LLMs can be powerful tools in the eval process, especially for categorizing errors and synthesizing information. However, they shouldn’t replace human judgment entirely. Use LLMs to assist with organizing thoughts and automating repetitive tasks, but always keep a human in the loop to ensure accuracy and alignment with product goals.

“”The goal is not to do evals perfectly. It’s to actionably improve your product.””

Building Automated Evaluators

Once you’ve identified failure modes through error analysis, the next step is to build automated evaluators. These can be code-based or use LLMs as judges. The goal is to create a suite of tests that run before deployment, ensuring your product meets quality standards. This automation saves time and resources, allowing you to focus on continuous improvement.

The Importance of Data Analysis

Data analysis is a powerful tool in the eval process. It helps you uncover unexpected errors and understand your product’s performance. By systematically analyzing data, you can prioritize issues and make informed decisions about where to focus your efforts. This approach leads to more effective and efficient product development.

Iterating on Evals

Evals are not a one-time task. They require ongoing iteration and refinement. As you gather more data and insights, you’ll need to update your evals to reflect new understanding and priorities. This continuous process ensures your product remains competitive and aligned with user needs.

“”Can’t the AI just eval it? But it doesn’t work.””

Evals as a New Skill

Evals have emerged as a critical skill for product builders, especially in AI. They provide a systematic way to measure and improve product quality. As more companies recognize their value, mastering evals can set you apart as a product builder and drive your product’s success.