On a Thursday afternoon, a product engineer tweaks the system prompt for a customer-facing summarization feature. The change looks good in the chat playground. Two teammates read three outputs and agree it sounds better. The PR merges. A week later, support notices that summaries have started omitting the action items.
Nobody wrote a test for "includes action items." Nobody had to - until now.
This is the gap evals are meant to close. Not a nice-to-have for ML researchers, but the basic quality infrastructure that any team shipping an LLM feature eventually needs.
What evals actually are
In classic software, you write a function, you write a test, and you know if it passes or fails. AI - especially LLMs and agents - doesn't play by those rules.
Outputs are non-deterministic, "correct" is often subjective, and the same prompt can produce different results each time.
Evals are the answer to that problem. They give you a systematic framework to measure model behavior across dimensions that matter - accuracy, relevance, safety, and task completion. Building evals is now a core engineering skill for any team shipping LLM features. Without them, you're guessing whether prompt changes help or hurt, whether model updates introduce regressions, and whether your system handles edge cases.
In practice, evals are not just measurement tools. They are the mechanism by which a team defines what "good enough" behavior looks like - and checks whether the system meets that standard over time.
Start with a golden dataset
The foundation of any eval pipeline is a golden dataset. A golden dataset contains trusted inputs and ideal outputs. These are typically hand-labeled by humans - often with domain expertise - and serve as a benchmark for model output quality.
For our summarization feature, a golden dataset might contain fifty real support tickets, each paired with a human-written ideal summary. The summaries were written by someone who knew the product: they include the customer's issue, the resolution, and - critically - any follow-up action items.
A golden dataset is a curated collection of input-output pairs that represent the expected behavior of your AI system. The key word is "curated." You're not scraping random examples; you're picking cases that cover the range of inputs your system will face, including the edge cases that tend to break things.
Building the golden dataset is the hardest part. It forces your team to agree, explicitly, on what a good output looks like - before you have a broken production feature teaching you what a bad one looks like.
Once you have the dataset, you run your prompt against it and compare the outputs to the expected answers. The comparison is where things get interesting.
Three ways to score an output
Teams generally reach for three kinds of scoring, often layered.
The first is deterministic checks: does the output contain a particular string, match a regex, fall within a word count? Fast, cheap, and unambiguous for structured outputs. Apply heuristic checks first. Before running expensive LLM judges, use deterministic metrics. If your summaries are supposed to end with a structured "Action items:" section, a regex will catch that before you spend a cent on inference.
The second is similarity metrics like ROUGE or cosine similarity against a reference output. These are fast and cheap, but often miss semantic equivalence. A model that rephrases "schedule a call" as "arrange a meeting" will score poorly on exact-match metrics despite being correct.
The third - and the one that has changed how teams work in the last two years - is LLM-as-judge. LLMs can act as evaluators across a variety of tasks, providing scalable and flexible judgment where human assessment is costly or impractical. You write a rubric - "does this summary include all action items mentioned in the source?" - and a separate, powerful model reads the source, the summary, and the rubric, then returns a score and a reason.
Once you have the golden dataset with labels, you can prompt an LLM to replicate human judgment as much as possible.
When the judge LLM reaches a certain accuracy with respect to the golden labels, it can be deployed in production to score any other similar dataset - for example, to evaluate the output of a target LLM.
Closing the loop
The eval pipeline isn't a one-time gate. Background monitoring evals run on live traffic or logs, without interrupting the core workflow. They track drift or performance degradation over time.
When something slips through - and it will - the fix feeds back into the dataset. When a judge flags a production response as a hallucination, that trace can be routed to a manual review queue. A human reviewer verifies the issue, adds the problematic input to the golden dataset, and uses it to retest the application after a fix. This closed-loop process is how you maintain quality as your application scales.
This is what some teams call evals-driven development. The idea - similar to test-driven development - means writing your evaluation metrics first, then adjusting the product (changing models, system prompts, or tool integrations). You run evals to ensure that the adjustments actually improve quality. If they don't, you continue iterating until you pass the evals.
A teammate like Beagle, sitting inside Slack, faces exactly this kind of invisible regression risk - the same system prompt runs across thousands of different messages and workspaces, so "it looked good in the playground" is not a quality bar that holds at scale. An eval suite against a golden set of real queries is the only way to ship prompt changes with confidence rather than hope.
The practical starting point
You don't need a hundred-case golden dataset on day one. You do not need a large program to see value. Start small, be precise, and iterate.
Pick one task. Write twenty examples where you know what good looks like. Write a deterministic check for any output property you can specify exactly. Write one LLM-as-judge rubric for the property that's hardest to specify but matters most. Run it before every prompt change.
That's the whole thing. The Thursday afternoon prompt edit that broke action items? It would have failed the rubric immediately, before the PR merged. The support tickets that followed would have stayed in the future tense - hypothetical failures, not real ones.