Picture this: you've written a function that calculates the square root of a number. Feed it the input 16, and you'll get 4 back every single time—guaranteed. This predictability is the bedrock of traditional software testing, where unit tests verify that each piece of code behaves exactly as expected with surgical precision.
Now imagine a system that, when asked to write a professional email, might produce dozens of different versions—all perfectly valid, but each unique in tone, structure, and word choice. Welcome to the world of Large Language Models (LLMs), where the very concept of "correct output" has become beautifully, frustratingly complex.
As LLMs have evolved from research curiosities to production-critical systems powering everything from customer service chatbots to code generation tools, a new discipline has emerged: LLM evaluations, or "evals." These aren't just traditional tests with a fresh coat of paint—they represent a fundamental shift in how we think about software quality assurance in the age of probabilistic AI.
When Deterministic Testing Meets Non-Deterministic Systems
Traditional unit testing operates on a simple premise: given a specific input, the output should be the same every time. This works brilliantly for conventional software, where functions are deterministic and predictable. A unit test for an e-commerce checkout function either passes (the order processes correctly) or fails (something breaks). There's no middle ground.
LLMs shatter this paradigm entirely. Unlike traditional software development where outcomes are predictable and errors can be debugged as logic can be attributed to specific code blocks, LLMs are a black-box with infinite possible inputs and corresponding outputs. When you ask ChatGPT to summarize a research paper, it might emphasize different key points each time, use varying levels of technical detail, or structure the information in completely different ways—and all these outputs could be equally "correct."
This non-deterministic nature isn't a bug; it's a feature. LLM systems can return multiple valid outputs for the same input, especially under real-world conditions, bringing creativity and flexibility that deterministic systems simply cannot match. But it also creates a testing nightmare that traditional approaches are fundamentally ill-equipped to handle.
Enter LLM Evaluations: The Evolution of Testing
LLM evaluations emerged as the industry's answer to this challenge, borrowing concepts from traditional testing while adapting to the unique characteristics of AI systems. Unit testing involves testing the smallest testable parts of an application, which for LLMs means evaluating an LLM response for a given input, based on some clearly defined criteria.
The key innovation isn't in the testing methodology itself, but in how we define and measure "correctness." Instead of looking for exact matches, LLM evals assess whether outputs meet certain quality criteria: Is the response relevant? Does it contain hallucinations? Is it appropriately formatted? Does it maintain the right tone?
Benchmarks are for model comparisons, Evals are for understanding the performance properties of the system, and Tests are for validating that those properties fall within acceptable bounds. This hierarchy creates a comprehensive framework where industry-standard benchmarks (like MMLU or HellaSwag) help compare different models, custom evaluations help understand how a system performs in specific contexts, and tests determine whether that performance meets production requirements.
The Anatomy of Modern LLM Testing
Contemporary LLM evaluation encompasses several approaches, each serving different purposes in the development lifecycle:
Code-based evaluations represent the closest parallel to traditional unit testing. A code-based eval is essentially a python or JS/TS unit test, checking for structured outputs, format compliance, or specific content requirements. These work well for tasks with clear right-or-wrong answers, like ensuring a JSON response contains required fields.
LLM-as-a-Judge evaluations leverage other AI models to assess quality in more nuanced ways. LLM-as-a-judge is a scalable and explainable way to approximate human preferences, which are otherwise very expensive to obtain. Instead of relying on exact string matches, these evaluations can assess semantic similarity, coherence, and contextual appropriateness.
Human-in-the-loop evaluations combine automated assessments with human judgment for subjective or safety-critical scenarios. Manual & Human-in-the-Loop Testing: Combines automated baseline assessments with human review for subjective or emergent issues, especially in safety-critical or ethical domains.
The most sophisticated approaches combine multiple evaluation types. A customer service chatbot might undergo code-based tests to ensure proper formatting, LLM-as-a-judge evaluations for response quality, and human reviews for handling sensitive issues.
Implementing Evals at Scale
Moving from theory to practice, LLM evaluations face unique implementation challenges. Reference-based metrics are more reliable than those without expected outputs, but creating comprehensive test datasets requires significant effort. Teams must balance the cost and complexity of evaluation against the need for reliable quality assessment.
The industry has responded with increasingly sophisticated tooling. Frameworks like DeepEval, OpenAI's Evals, and Promptfoo have emerged to make LLM testing more accessible. DeepEval is a simple-to-use, open-source LLM evaluation framework, for evaluating and testing large-language model systems. It is similar to Pytest but specialized for unit testing LLM outputs.
These tools enable what many consider essential: automated testing in CI/CD is crucial especially in a team environment to prevent unnoticed breaking changes. Just as traditional software development relies on continuous integration to catch regressions, LLM applications need automated evaluation pipelines to ensure changes don't degrade performance.
The Art of Measuring the Immeasurable
Perhaps the most fundamental challenge in LLM evaluation is defining what "good" means in the first place. A unit test is a specific, clear, testable statement or question in natural language about a desirable quality of an LLM's response. This natural language approach, pioneered by tools like LMUnit, allows developers to express complex quality criteria in human-readable terms.
Questions like "Is the response succinct without omitting essential information?" or "Is the complexity of the response appropriate for the intended audience?" capture nuanced requirements that would be nearly impossible to encode in traditional test assertions.
The industry is also grappling with the reliability of evaluation itself. LLMs often struggle with the subtleties of continuous scales, leading to inconsistent results even with slight prompt modifications or across different models. This has led many practitioners to prefer categorical evaluations over numerical scores, as they provide more consistent and interpretable results.
The Future of AI Quality Assurance
As LLM capabilities expand beyond text generation into agentic behaviors—systems that can plan, execute actions, and modify their strategies—evaluation approaches continue evolving. Instead of evaluating outputs in isolation, agents will be tested in simulated environments, where success is measured not by text correctness but by goal achievement.
This shift represents a fundamental change in software testing philosophy. We will combine deterministic unit testing (for agent state, API bindings, tools) with stochastic behavior testing (for plan generation, user-facing output). Future AI systems will require hybrid approaches that maintain the reliability guarantees of traditional testing while accommodating the creative unpredictability of AI.
The implications extend beyond AI development. As more conventional software incorporates AI components, the boundaries between deterministic and probabilistic systems will blur. Understanding LLM evaluation techniques today prepares developers for a future where all software testing must account for some degree of non-deterministic behavior.
The evolution from unit tests to LLM evals represents more than a technical adaptation—it's a recognition that as our tools become more intelligent and autonomous, our methods for ensuring their reliability must become equally sophisticated. In a world where software increasingly thinks for itself, the art of testing is being reborn for the age of artificial intelligence.
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