AI
According to studies, 87% of AI initiatives fail not because of bad models, but because of low data quality, misalignment with business needs, and because teams can't reliably measure what "good" looks like in production (i.e. unable to monitor, measure and improve system quality in production). The secret weapon of AI leaders isn't faster development, it's building scalable evaluation infrastructure that drives quality.
Eval is short for evaluation. Evals are analogous to automated tests in traditional software systems.
While organizations race to develop and deploy AI models, a silent but critical factor often determines success or failure: evaluations. Evals are the foundation of trustworthy, reliable, and scalable AI. Done well, they close the gap between experimental models and valuable production systems. Done poorly, or neglected entirely, they are why AI projects routinely join the 87% that never deliver business value.
So, what exactly is an eval? In the context of Large Language Models (LLMs), an eval is a systematic method for measuring a system's ability to meet predefined quality benchmarks. Think of evals as the equivalent of test automation in traditional software. They are a set of intentional checks that determine if your system is performing as expected. But with AI, testing is more complicated than with traditional / deterministic systems.
Small evals are similar to unit tests and are the first line of defense. They evaluate individual components within your AI system, such as a specific prompt within a multi-prompt agent, or a particular function (like a tool call) invoked within a larger pipeline. These tests are similar to unit tests in traditional software: they're fast, focused, and help spot flaws early in development. Sometimes, a unit eval might be as simple as checking that one node within a larger agent model consistently returns the correct result for a controlled input.
Large evals are equivalent to integration tests and operate at the system level. They are more complex, slower to run, and mirror integration tests from the world of software engineering. These evals target the parent agent and assess full workflows to track how multiple components interact and whether the overall outputs meet user and business needs. They are essential for surface-level testing of the finished product, capturing emergent behaviors that only appear when components interact in realistic conditions.
What makes evals for AI vastly more difficult than tests for traditional software? Determinism. In a conventional application, a function like sum(3,5) returns 8 every time. When you write tests you are guaranteed that the function will return the same result. But LLMs and modern AI systems are inherently non-deterministic which means that the same input can result in a range of valid outputs, even when the model is not updated.
This means that two equally correct AI answers may look completely different! Robust evals must account for this ambiguity. Classic pass/fail logic falls short, so evaluation strategies need to measure things like semantic similarity, relevance, factuality, safety, or business policy alignment, not just exact matches. This makes designing good evals a creative and ongoing effort.
Evals are essential in maintaining quality in an AI system. The unpredictability of AI outputs means that without automated, properly designed eval strategies, you simply cannot guarantee system stability or reliability. Their criticality is magnified in AI, where a missed bug could lead to embarrassing, risky, or costly errors.
Before any deployment, comprehensive evals are the only way to safeguard business processes, mitigate risks, and provide stakeholders with confidence. They help convert AI from an unpredictable experiment into a robust production tool.
What separates thriving AI teams from the rest is not just their models and prompts, but their relentless focus on evaluation. By combining small evals (unit tests) with large evals (integration tests), embracing AI’s inherent non-determinism, and building automated, scalable infrastructure for evaluation, you’ll move your organization out of the AI "failure majority" and into the sphere of reliable, value generating AI innovation.
AI evaluation isn’t a checkbox at the end of your workflow - it’s a core process that ensures your AI delivers consistent, real-world value.