- AI evals are structured rubrics that grade model outputs, giving teams a repeatable way to measure and improve quality.
- Without evals, AI improvement is guesswork. With them, you can pinpoint exactly what is failing and why.
- Models change constantly. Eval skills do not. They are the highest-ROI, most durable skill in AI right now.
There is a new LLM every week. A new agent framework every month. Over a hundred research papers are hitting arXiv on any given day. For anyone working at the intersection of business problems and AI systems, keeping up feels less like staying informed and more like drinking from a fire hose.
But here is the thing: you cannot out-read the fire hose. What you can do is out-evaluate it.
AI model evaluation, or “evals,” is one of the most practical and durable skills a practitioner can build right now. It applies across every type of AI system, it compounds over time, and unlike familiarity with any specific model or framework, it does not go stale when the next release drops.
What Are AI Evals?
Nazir’s definition is deliberately simple: think of evals like pop quizzes for your model. You have an input. The model produces an output. The eval is where you grade that output against a rubric. Simple in concept, but the execution is what separates teams that improve systematically from teams that are guessing.
He illustrates this with a concrete example from his time at Alexa. The platform was handling roughly 100 million utterances per day, and among the most common were questions like “Can dogs eat apples?” or “Can dogs eat chocolate?” The model’s answers sounded reasonable on the surface, but when annotators graded them, the issues became clear fast.
A response of “yes” to “Can dogs eat onions?” might seem technically responsive, but it is dangerously incomplete. The correct answer is that dogs cannot eat onions at all. A response of “yes, but only in small amounts” is worse, the model sounds authoritative while being wrong.
“The model answers, it sounds smart, but they’re not always correct or complete. This is where evals come in. They help you measure what quality means for those answers.”
In practice, an eval pipeline starts with open codes: initial human reactions like “correct,” “partially correct,” or “insufficient detail.” Over time, those reactions get structured into formal rubrics and eventually automated. The core principle stays the same throughout: if you can measure output quality consistently, you can improve the model systematically.
Where Evals Fit in the AI Lifecycle
Evals are not a new invention. The concept applies across all five generations of AI that Nazir outlines from legacy rule-based systems in the 1990s through classical machine learning, neural networks, large language models, and now agentic AI where multiple LLMs interact with each other. The framing shifts depending on what kind of AI is being built, but the underlying logic is constant: a structured, repeatable way of assessing output quality.
For the purposes of the series, the focus is on generative and agentic AI — types four and five in his taxonomy. The reason is practical: evals learned in that context transfer forward as the field moves, and they do not become obsolete when the next model is released.
Within the AI product development lifecycle, Nazir points to the CRISP-DM framework, which refers to a six-stage model covering business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Evals land squarely in stage five, and they serve a binary function: either the model is good enough to deploy, or it needs another iteration. But the evaluation does not stop at deployment. At Alexa, annotators were grading a fraction of those 100 million daily utterances every single day, in production, continuously. That is what a serious eval looks like at scale.
Why AI Model Evaluation Is the Highest-ROI Skill Right Now
Nazir is direct about the stakes. Without evals, AI development is guesswork. Teams might sense that something is off with model performance, but they cannot identify where, why, or what to fix. With evals, those questions become answerable.
The production failure rate for generative AI projects makes this concrete. An MIT study found that roughly 95% of gen AI projects fail in production. A significant portion of those failures come down to ROI not materializing and performance not meeting expectations, which are problems that a structured evaluation practice directly addresses.
“If there’s only one reason for you to master evals, it’s this: they give you the highest return on investment on any type of AI performance improvement.”
This matters beyond the technical team. Even practitioners who are not deeply technical benefit from understanding evals, because knowing how to evaluate AI output means being able to direct engineers toward fixes that actually matter rather than optimizing for the wrong signal.
There is also a career dimension that Nazir raises directly. The AI landscape changes fast. Models that are state-of-the-art today will be superseded in months. Skills tied to a specific model or architecture have a short shelf life. Eval skills do not. They are durable precisely because they are model-agnostic.
Models Are Temporary. Evals Are Forever.
The clearest illustration of this principle comes from the ImageNet challenge, a computer vision competition that ran from the early 2010s and asked models to classify images across 1,000 categories with fine-grained precision. Classifying an image as “dog” was not enough. The model had to identify the breed and sub-breed correctly. Getting a Siberian husky classified as an Alaskan husky counted as an error.
In 2011, the top-five error rate across competing models was around 26%. AlexNet dropped that to roughly 15% in 2012. By 2015, models had surpassed human-level performance on the same benchmark. The models that achieved this were entirely different architectures from the ones that started the competition. Each generation made the previous one obsolete. But the evaluation metric, top-5 precision, remained constant throughout. It was the fixed point around which model progress was measured and understood.
“Models come and go, but evals stick around. They are your compass in this fast-moving AI jungle.”
That is the argument in its most distilled form. Whatever model is current today will be replaced. The ability to evaluate whether a model is doing what it is supposed to do — rigorously and repeatably — is what compounds over time.
What This Means for AI Practitioners in 2025 and Beyond
The online learning space for AI is crowded and often inconsistent. Courses are either too surface-level or too academic. Hands-on programs frequently lack the context and career guidance that practitioners actually need. Nazir’s frustration with that gap is what draws him to teaching at Interview Kickstart, where the program is designed to be structured, hands-on, and taught by practitioners who have shipped AI systems at scale.
For anyone looking to build a durable, high-value skill set in AI, understanding how to evaluate model outputs is not optional — it is foundational. It is what separates teams that iterate intelligently from teams that ship and hope.
Interview Kickstart’s Agentic AI Career Boost Program is built for this moment. Engineers follow a Python-based AI engineering path. PMs and managers take a no-code, low-code use case track. Both paths include FAANG-level interview preparation for AI-driven roles, with mentorship from practitioners at companies like Google, Meta, Amazon, and Anthropic. You build and ship two AI agents into production across the program, guided step by step.
The models will keep changing. The skill of knowing whether they are working will not go out of style.
FAQs
1. What is AI model evaluation?
It is the process of grading AI outputs against a defined rubric to measure quality, catch errors, and identify where the model needs improvement.
2. Who needs to understand AI evals?
AI product managers, ML engineers, AI engineers, and data scientists basically anyone deciding what AI to build and whether it is working.
3. When in the AI development lifecycle do evals apply?
At stage five of the development cycle before deployment, and then continuously in production. Evals do not stop once a model ships.
4. Do evals become outdated as new models release?
No, that is the point. The ImageNet challenge used the same eval metric from 2011 through multiple model generations. The models changed; the eval stayed.
