Product managers, developers, and even executives in the C-suite are now trying to figure out the best way to test AI systems. In a lot of ways, this is like the early days of software engineering, when "test-driven development" was the norm.
Evals are now an important part of how companies check the performance and dependability of large language models (LLMs). They work in the background and let you know if your AI is doing what it's supposed to do and if you can trust it to do it on a large scale. But evals are still a bit of a mystery to many companies that are looking into AI products or adding LLMs to systems that are already in use. What exactly are they? How do you design the right ones? And why are they suddenly so essential to AI development?
At its simplest, an eval is just a test. It asks a straightforward question: How well does your model or agent perform in a given context? But behind that simple question lies an intricate process of experimentation, iteration, and alignment. Designing good evals requires both technical insight and a deep understanding of the human outcomes they’re meant to measure.
These tests are now essential for every company, whatever the objective, improving the customer experience, automating internal processes, or ensuring compliance, evaluations reveal how confidently a company can go from prototype to production.
Why AI Needs Evals
You type a question, get an answer, and sometimes it’s exactly what you wanted, other times it’s not even close. Every large language models user has experienced it. That inconsistency is a fundamental property of how these models work.
LLMs are nondeterministic systems. The same prompt can yield different outputs each time it’s run, even under identical conditions. That randomness is part of what makes them powerful but it also makes them unpredictable. Evals are how we bring structure to that unpredictability. They help us understand if a model performs within acceptable bounds and whether it can be trusted to behave as intended.
An eval defines the framework for testing how a model responds to a specific prompt or task. In its simplest form, it answers the question: Given this input, does the model produce a response that meets our expectations? But in practice, it’s more than a yes-or-no test. It’s about defining what “good” actually means for your use case (Is it accuracy, reliability, truthfulness, safety, etc.) and measuring the model’s performance against those standards.
Scale adds to the complexity. Every day, enterprises and AI providers process massive volumes of prompts and responses. Each interaction is an opportunity for improvement, but evaluating each would be prohibitively expensive. Many organizations now use smaller, less expensive models to review or score outputs, reserving more thorough evaluations for critical or high-risk situations.
The trade-off between granularity and cost underpins much of modern AI evaluation strategy. Companies must determine how detailed their feedback loops should be and where it makes sense to invest in additional oversight. For example, in a general-purpose chatbot, occasional errors may be acceptable. In a financial services application, they are not.
In regulated industries like banking and insurance, businesses are implementing AI systems that interact directly with customers, sometimes even handling sensitive tasks like disputes or claims. In these cases, the margin of error is vanishingly small. A single incorrect or misleading response can have serious consequences, including reputational damage and regulatory scrutiny.
To avoid this, organizations use evaluations to create observability, which provides a clear picture of what the AI system is doing, why it is doing it, and whether those actions are consistent with compliance and policy requirements. The goal is not only to measure performance, but also to predict failure modes before they enter production.
A well-designed evaluation pipeline may begin with lightweight checks that detect simple issues early on, before progressing to more comprehensive evaluations for edge cases. This layered approach promotes efficiency and control. It promotes that when a model deals with sensitive information, such as financial guidance or a customer complaint, the organization knows exactly how it is performing and can trace it back to specific parameters or prompts.
Designing Effective Evals in the Enterprise
Companies operating in industries with strict regulations must stick to all regulations without exception. Marketing messages, chatbot responses, and voice agents resolving consumer disputes are all subject to the same set of legal and ethical requirements. Problematically, these rules change all the time and AI systems must find a means to adapt.
Building a good eval starts with understanding people. Before automating anything, organizations need to map the human decision-making process behind their product. What are the outcomes they care about? What does “good” look like in a conversation? What behavior must never happen under any circumstances? This kind of “human problem engineering” forms the foundation of meaningful evaluations.
Only once those human expectations are clearly defined can they be translated into a codified, scalable process. In many cases, that translation takes the form of smaller language models that act as monitors, checking whether an AI system’s output meets certain criteria in real time.
In regulated settings like finance or insurance, certain evaluations have to happen before an AI agent takes action or communicates a response. These are known as online evaluations, and they function almost like guardrails. When a customer service agent, or an AI agent, handles a request, the system can automatically trigger an evaluation before a message is sent. The evaluation might use a lightweight language model to review the conversation so far and check whether the response risks violating a compliance rule. If a potential violation is detected, the system can escalate the case to a human reviewer.
For other types of interactions, evaluations can occur after the fact. These offline evaluations are used to assess overall quality, tone, and performance trends across large volumes of conversations. They help teams understand how well the system behaves over time and where improvements are needed. Both online and offline approaches work together to maintain control and accountability at scale.
During long interactions, models can gradually lose track of earlier details in the conversation, a phenomenon often called “context rot.” When that happens, even well-trained systems may begin to stray from the rules or forget earlier instructions. Evals can be configured to review ongoing transcripts periodically, checking whether the model continues to follow the intended guidelines and maintaining the standard of behavior expected from the system.
Metrics and Continuous Improvement
Once an evaluation framework is established, the next question is how to assess success. In most systems, evaluators (human or model-based) produce simple results that can be combined to yield meaningful insights. Sometimes it's a binary decision: did the agent follow the policy? In other cases, the result could be classified as low, medium, or high risk. These metrics assist teams in understanding not only isolated errors, but also larger patterns of performance. Enterprises can monitor model behavior over time and identify areas for intervention by tracking the frequency of noncompliant or subpar interactions.
This type of continuous measurement is the foundation of model improvement cycles. Evaluations are continuous checkpoints that guide each update and prompt adjustments. In a well-designed system, each prompt version, system message, or tool description is tested against a comprehensive set of evals before and after any changes. The goal is to determine exactly how a modification affects performance across multiple dimensions—accuracy, tone, compliance, and alignment with the ground truth data.
In practice, this looks very similar to A/B testing for AI. When a product manager or research team wants to change a prompt, they compare the results of the old and new versions under controlled conditions. If the updated configuration outperforms the previous one on the right metrics, it becomes the new baseline. If not, it is revised again. This process is similar to the disciplined iteration cycles that have long defined software engineering, except that it now applies to language models and agent behavior.
Evaluations Keep AI on Track
The ideal state for most AI-driven organizations is one of timely version control and transparent evaluation history. Each change is documented, tested, and validated using a structured evaluation process.
Some industries, particularly legal and financial services, are already approaching this ideal. These organizations typically have well-defined evaluation dimensions and sufficient data coverage to assess performance with precision. Each prompt update initiates a battery of tests that ensure adherence to both internal policies and external regulations. The end result is an AI development process that is as transparent and accountable as any other enterprise system.
On the other end of the spectrum, many rapidly growing technology companies are still experimenting. Their AI agents work in open-ended, dynamic environments in which behavior is difficult to predict and benchmark. In those cases, evaluation frequently relies on exploratory testing, which involves observing how models behave in real-world interactions rather than running through predetermined test sets. Both approaches are valid, depending on the situation. What matters is that the evaluation occurs at all.
That's because the alternative—creating and deploying AI without evaluations—is a risk few can afford. The notion that "evals are dead," a phrase that occasionally appears in developer circles, completely misses the point. Evals enable scale. Without them, there is no systematic approach to understanding performance, improving reliability, or demonstrating compliance.
Why Evals Move AI Beyond the Hype Cycle
In most companies, the decision to implement AI is first made at the executive level. There is a sense of urgency among executives to implement AI systems ahead of their competitors due to competitive pressure and industry momentum. The "fear of missing out" is reasonable and nobody wants to be left behind. However, in their hurry to get the systems live, many businesses learn the hard way that even the most sophisticated AI systems can unexpectedly crash in a lack of enough supervision.
That realization is often the turning point. After the first few missteps, teams recognize that observability—understanding what the system is doing and why—must be built in, not bolted on. This is where evals emerge as the missing piece between experimentation and production.
Before organizations reach that maturity, however, there’s often confusion between benchmarks and evals. The two sound similar but serve entirely different purposes. Benchmarks are standardized tests that measure how models perform “out of the box.” They’re useful for comparing different models—how a provider’s large language model fares against another’s on a given task. When a new model version launches, its benchmark scores help the industry understand its general capabilities.
Evals, by contrast, are about your product, not the model itself. They measure how well an AI system performs within a specific use case, under the particular conditions and prompts you’ve designed. They’re the mechanism that lets teams measure progress, diagnose weaknesses, and iteratively improve reliability over time.
For product teams, this distinction matters. Evals are the foundation of product governance. They give AI builders a way to understand the impact of every prompt change, dataset adjustment, or workflow update. In other words, they let you measure what actually improves the user experience.
The First Step for AI-Ready Organizations
Actually, a lot of businesses are already doing some kind of small-scale experimentation here and there. Informal AI tool usage is increasing productivity among developers, analysts, and customer service representatives. Establishing proper governance, data controls, and observability is typically the firststep in formalizing such grassroots efforts.
Teams can then start choosing use cases that have a good mix of low risk and high potential for learning. To gain insight into agents' field behavior, organizations should begin with contained, low-stakes use cases. These use cases could include helping with internal documentation, summarizing data, or providing basic support for customer inquiries. It's an opportunity to refine workflows, test eval infrastructure, and build confidence within the organization.
Making the transition from ideation to implementation can be a real challenge. Learning by doing is still crucial for AI productization, even though there are many published frameworks and best practices. Customer contexts, policies, and data are unique to every business. The trick is to take baby steps, watch closely, and change things up fast.
In addition to investigating high-impact, long-term changes, some organizations are testing low-risk scenarios; this is especially true in regulated industries. For example, when AI agents work in tandem with human teams, we may need to rethink how we do things like process legal documents, financial advice, or insurance claims.
A data-driven feedback loop is provided by continuous evaluation, which makes AI scalable, safe, and reliable, whether the goal is small-scale optimization or large-scale reinvention.
How Solwey Can Help
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That's where Solwey comes in.
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