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When it comes to deploying AI systems, one lesson from the software world rings especially true: you need to test what you build. In traditional software development, nobody would ship a major feature without running it through a suite of tests. Yet in the rush to implement AI, some teams treat their model’s outputs as magic without rigorous verification. This is where evals come in. “Evals” is shorthand for evaluations – systematic tests of an AI model’s performance, accuracy, reliability, and safety. In simple terms, an eval is how you measure whether an AI is doing what it’s supposed to do. Rather than trusting a gut feeling or a few anecdotal trial runs, teams are increasingly using evals to get hard evidence of an AI’s capabilities and weaknesses. Just as a car undergoes crash testing before being sold, an AI model should undergo evals before it’s deployed for real-world use.

Why Evals Matter

AI models can be impressively human-like in their answers, which ironically makes it easy to be overconfident in them. The truth is that even advanced AIs have failure modes – they can get facts wrong, produce inappropriate content, or break when given unusual inputs. Without evals, these problems might only surface after your AI is in production, potentially causing user harm or business liability. A stark example was an airline’s customer service chatbot that was asked about bereavement fares (discounts for flights after a family death). Lacking proper evaluation, the AI hallucinated a fake policy – it made up an answer that sounded plausible but was completely incorrect. The customer, unfortunately, acted on this bad info, and the airline ended up facing a PR nightmare and legal troubles. The lesson? “It seems to work” is not a strategy. Evals are the safety net to catch such issues early. They give you confidence in your AI by systematically probing it: not just for right answers, but for resilience under stress. Imagine you’re deploying an AI assistant for financial advice – you’d want to evaluate how it handles edge cases (like volatile market scenarios), whether it occasionally gives dangerously wrong suggestions, and if it stays within compliance boundaries. Conducting evals that target those points can reveal if the model is ready or if it needs more training/guardrails. In essence, evals distinguish a clever demo from a production-ready system. They force clarity on success criteria and align everyone (developers, product managers, executives) on what “good performance” actually means.

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What Do Evals Look At?

Because AI behavior can be complex, evals typically examine multiple dimensions of performance. It’s not as simple as a pass/fail on one question; a robust eval checks for a variety of quality markers. Key questions include:

Accuracy: Did the model produce the correct or expected answer for the task?
Instruction-following: Did it follow the user’s instructions and the intended format or style?
Hallucination check: Did it refrain from making up facts or information that wasn’t provided or known?
Safety & compliance: Did the output avoid forbidden content or biased/offensive responses, adhering to ethical guidelines and any regulatory constraints?
Reliability: Does the model give consistent, repeatable results, or does it behave erratically on similar inputs?
Efficiency: Was the response produced within an acceptable time frame and cost? (This is especially important if using a paid API or if users need real-time answers.)

Notice that some of these are objective (e.g. factual accuracy) while others are more subjective or context-dependent (e.g. was the tone appropriate?). That means evals often combine quantitative tests with qualitative reviews. For instance, you might have a set of Q&A pairs where you know the correct answers – the model can be scored on how many it gets right. That covers accuracy. But you might also have human reviewers rate a batch of outputs for clarity, tone, or presence of bias – covering aspects that are harder to boil down to a single “right answer.” In recent years, companies like OpenAI have even open-sourced entire eval frameworks (e.g. OpenAI Evals in 2023) so that the community can contribute to testing AI models. The field has converged on the idea that a multi-metric evaluation is necessary: an AI that’s 100% accurate but spews sensitive data or takes 60 seconds to respond is not viable. Evals help paint a complete picture of a model’s performance across all these axes.

Origins and Evolution of Evals

The practice of evaluating AI systems has its roots in academia. Researchers have long used benchmarks – standardized datasets and tests – to compare models for tasks like translation, image recognition, or reading comprehension. These were essentially early “evals,” although they weren’t called that at the time. Over time, as AI models became more general and powerful (like today’s large language models), the need for diverse and domain-specific evals grew. The term “evals” gained popularity around 2023 when leading AI labs began sharing their evaluation frameworks openly (for example, OpenAI open-sourcing a library called “Evals”). Suddenly, evaluation became a collective effort, not just an internal exercise. By 2025, “evals” became a core part of any serious AI project’s lifecycle, with dedicated evaluation teams and tools (much like QA teams in traditional software). There’s even talk of “EvalOps” – the idea that Evaluation Operations should be a formalized function just like DevOps or MLOps, ensuring that models are continuously tested and monitored even after deployment.

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The Future: Continuous Evaluation

Future evals won’t be a one-time checklist – they’ll be continuous and proactive. Teams are beginning to integrate evaluation tools into live AI systems, with dashboards that monitor an AI’s performance in real time and alert developers to any drift in quality or safety. We can envision AI “auditors” that constantly simulate user interactions, searching for weaknesses or policy violations 24/7. In high-stakes applications (say, an AI in healthcare or finance), such ongoing evals will likely become mandatory, providing regulators and stakeholders with confidence that the system remains reliable over time. Evaluation will evolve into a dynamic process that adapts as the AI and its users do – catching new failure modes and ensuring the AI keeps meeting its standards long after deployment.

Conclusion: No Deployment Without Evals

As AI becomes a backbone of more products and decisions, evals are turning from a best practice into a necessity. They are the mechanism by which we build trust in AI – for developers, businesses, and users alike. Just as no responsible engineer would launch code without testing, no AI team should feel comfortable launching a model without thorough evals. It’s not about adding bureaucracy; it’s about responsibility and risk management. Rigorous evaluations shine a light on the model’s blind spots and failure modes so they can be addressed early, or at least understood and mitigated. They also provide a common language (metrics and results) that helps tech teams communicate with leadership and customers about how the AI will perform. In the end, doing evals is about recognizing that an AI system is only as good as the tests it passes. If we want these systems to be safe, reliable, and effective, we have to put in the work to measure those qualities. The organizations that embrace evals are essentially saying: “We don’t just hope our AI works well – we’ve proven it.” And that can make all the difference between an AI rollout that ends in disappointment and one that delivers on its promise.

References (Evals)

OpenAI (2023). OpenAI Evals Announcement. – OpenAI’s open-source release of the “Evals” framework, highlighting a crowdsourced approach to evaluating AI models and encouraging community-built benchmarks.
Collicutt, C. (2025). “What are Evals?” – Article explaining AI evaluation and testing, covering the importance of assessing LLM performance and describing different types of eval methods (human feedback, code-based checks, and AI-driven evals).
Diaz, H. (2025). “Why You Need to Start Thinking About AI Evaluation Operations Now.” – Discusses the risks of deploying AI without proper evals (including a real incident of a chatbot’s mistake), and paints a vision of continuous “EvalOps” with real-time monitoring of AI systems.
PromptQL Blog (2025). “Evals 101 for Executives.” – Outlines how evals serve as the connective tissue between business objectives and AI performance, advocating for using evals as a strategic tool to define success criteria and measure ROI of AI projects.

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