Why everyone’s confused about evals

Key Concepts

• Evals (Evaluations): Often used as a blanket term, but encompasses distinct processes – Error Analysis (typically done by data labeling companies) and building LLM Judges (more complex, often discussed in the context of Product Management).
• Error Analysis: Identifying and categorizing mistakes made by an AI model, usually performed by human annotators.
• LLM Judges: AI models specifically trained to evaluate the output of other AI models, aiming for automated and scalable evaluation.
• Actionable Feedback Loop: A continuous process of gathering data on AI performance, analyzing it, and using the insights to improve the model.
• Context Dependence: The understanding that the best approach to AI evaluation and improvement varies significantly based on the specific application.

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Understanding the Nuances of “Evals” in AI Development

The discussion centers around the frequently misused and often conflated term “evals” within the AI/ML development lifecycle. The core argument is that the term is used differently by various stakeholders – data labeling companies, Product Managers (PMs), and ML practitioners – and understanding these distinctions is crucial for effective AI product development.

Data labeling companies, when stating their “experts are writing evals,” are almost exclusively referring to error analysis. This involves human annotators meticulously reviewing model outputs and categorizing the types of errors made. This is a crucial step in understanding what the model is getting wrong, but it doesn’t necessarily involve building sophisticated automated evaluation systems. The analogy provided highlights this: just because lawyers and doctors write evaluations in their respective fields doesn’t mean they are constructing AI-powered judging systems.

Conversely, when PMs suggest they should be “writing evals,” the implication is often about establishing a system for ongoing performance monitoring and improvement – potentially involving the creation of LLM Judges. However, the speaker clarifies that PMs aren’t necessarily expected to build production-ready LLM judges themselves. Their role is more about defining the requirements and overseeing the process.

The Importance of Actionable Feedback Loops

A central tenet of the discussion is the universal agreement among ML practitioners regarding the necessity of an actionable feedback loop for AI products. The speaker posits that if you gather a group of ML professionals and ask if building a feedback loop is important, the consensus would be overwhelmingly positive. However, the how of implementing this loop is highly dependent on the specific application.

This leads to the key point that there is no one-size-fits-all solution. The speaker emphasizes that the optimal approach to evaluation and improvement is deeply rooted in the context of the AI application.

Avoiding Prescriptive Approaches

The speaker strongly cautions against becoming “obsessed with prescriptions.” The field of AI is rapidly evolving, and methodologies that are effective today may become obsolete tomorrow. The statement, “it really depends on the context,” is presented as a mantra for ML practitioners. Rigid adherence to specific frameworks or tools can hinder adaptability and ultimately impede progress. The speaker anticipates that established “prescriptions” will inevitably change, reinforcing the need for a flexible and context-aware approach.

Synthesis

The core takeaway is a call for clarity and nuance in discussions surrounding AI evaluation. The term “evals” is ambiguous and should be unpacked to understand whether it refers to error analysis, the development of LLM judges, or the broader establishment of an actionable feedback loop. Furthermore, successful AI product development requires a context-dependent approach, avoiding rigid adherence to prescriptive methodologies and embracing continuous adaptation. The emphasis is on understanding the why behind evaluation, rather than blindly following a specific how.