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Evals

Also written: evaluations, Evaluate function, Frontier evals, Empirical evidence, AI evaluation methodologies, AI assessment methodologies, Eval scores, Evaluation scores

Evals are tests to measure how well AI models or agents perform against specific goals.

In plain terms

Evals, short for evaluations, are tests designed to check how well an AI system is working. Think of them like a report card for AI. Instead of just asking an AI a question and seeing if the answer seems right, evals use a set of specific questions or tasks to measure its performance in a structured way.

For example, if you're building an AI to help customers with technical support, you wouldn't just ask it a few questions. You would create a set of common customer problems and see how often the AI provides the correct solution, how quickly it responds, and if it follows the right steps. This gives you a clear, measurable way to know if the AI is good enough to use.

The expert version

Evals, or evaluations, are systematic processes for assessing the performance, capabilities, and behavior of AI models or systems against predefined criteria. They move beyond anecdotal observation to provide quantitative or qualitative metrics on aspects like accuracy, relevance, safety, and adherence to instructions. Effective evals often involve curated datasets, benchmark tasks, and human review to ensure robust measurement of desired outcomes and identification of failure modes.

The design of evals is critical, as poorly constructed ones can lead to misleading conclusions about an AI's true capabilities. Tradeoffs exist between the cost and complexity of creating comprehensive evals and the confidence they provide in model performance. For instance, evaluating complex AI agents often requires specialized evals that consider emergent behaviors and interaction dynamics, which are harder to capture than simple accuracy metrics.

Why it matters now

Frontier AI labs are increasingly focused on making their models reliable and production-ready. This involves rigorous testing and evaluation to ensure AI agents perform as expected, especially for critical tasks. Companies are developing tools and methods to systematically assess AI capabilities beyond simple prompt adjustments.

Where it shows up 30 items, newest first
How each company uses it
Google Gemini
Google Gemini highlights building AI agent evaluations systematically for production use, not just prompt tweaking.
OpenAI ChatGPT
OpenAI offers tools like the Responses and Evals APIs for building and improving AI agents and regression tests, as seen in their Builder Bootcamps.
xAI Grok
xAI Grok is enabling automated AI agent debugging and metric verification, suggesting a focus on systematic evaluation.
Related terms

AI AgentsBenchmarkingMetricsTestingPerformanceSafety

Often confused with
Testing
Testing is a broad term for checking if software works, while evals are
Benchmarking
Benchmarking compares an AI's performance against established stan
Metrics
Metrics are the specific measurements used within evals to quantify per
Origin

The term 'evals' is a common abbreviation for 'evaluations' within the fields of computer science and artificial intelligence, referring to the process of assessing system performance. Its specific application to AI models and agents has become prominent as these systems have grown in complexity.

Try this about 5 minutes

Try asking an AI chatbot like ChatGPT, Claude, or Gemini to write a short story about a robot detective. Then, ask it to rewrite the story in the style of a specific author. You can informally 'eval' how well it followed both instructions.

Try it: Playbook entries that use thisPlaybook →

The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.

Questions people ask

What is the difference between an eval and a test?
An eval is a specific type of test designed to measure AI performance against defined criteria, often involving structured datasets and metrics. General testing might be broader, checking for bugs or basic functionality.
Why are evals important for AI agents?
Evals are crucial for AI agents to ensure they behave reliably, safely, and effectively in complex, real-world scenarios before deployment, preventing unexpected or harmful outcomes.

First seen on Subvolts Sep 16, 2024. Updated Oct 15, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.