How should teams get started building effective evals for LLM systems?

How to Start Evaluating LLM Outputs Without Waiting for a Perfect Benchmark

The fastest way to make evals useful is to treat them as a decision-support loop, not a one-time measurement exercise. Teams usually get value sooner by testing a small set of real examples, comparing machine scores with human review, and refining the rubric where disagreement appears. That approach is especially important for LLM systems because quality is often contextual: the same answer can be acceptable for one user task and unacceptable for another.

Starting small also prevents teams from overfitting to synthetic or overly neat datasets. A compact initial set makes it easier to inspect failures, spot ambiguous criteria, and decide what the system should optimise for first. The objective is not statistical completeness on day one; it is to build a repeatable process that reveals where the model is dependable and where it still needs guardrails. For broader governance of AI risk and evaluation discipline, NIST’s NIST AI Risk Management Framework gives a useful backbone for aligning measurement with risk management rather than vanity metrics.

In practice, many teams discover that the first useful evals emerge only after they compare scores against real reviewer disagreement, rather than after they finish building a larger test set.

What Good Early Evals Look Like in Practice

Effective early evals usually start with a narrow slice of the system’s actual workload. That means selecting examples that reflect common user intents, known failure modes, and high-consequence cases, then writing criteria that a human reviewer can apply consistently. The criteria should be explicit enough that two reviewers can disagree for a reason, not because the rubric is vague. If the system is used for summarisation, retrieval, classification, or assistant-style responses, each task needs its own evaluation lens because “good” means something different in each case.

A practical workflow is to score the same sample set in two ways: automated evaluation and human judgment. When those signals diverge, the disagreement is often more valuable than the score itself because it shows where the rubric is under-specified, where the model is gaming the metric, or where the task is too ambiguous for a single number. Teams should then revise the criteria, not just the output. The point is to improve the measurement definition alongside the model.

A useful early structure is:

• define one task at a time rather than measuring the whole system at once;
• choose examples from real user traffic or credible near-real usage;
• label a small set with clear pass, fail, or graded criteria;
• compare automated scores to human review on the same examples;
• record where disagreements come from so the rubric can be tightened.

For teams building evaluation discipline around generative AI systems, the NIST AI 600-1 Generative AI Profile is useful because it links evaluation work to generative AI risk considerations rather than treating scoring as a purely technical exercise. Where the evaluation covers agent-driven workflows, the OWASP Agentic AI Top 10 can help teams think about failure modes that basic quality scores can miss, such as unsafe tool use or brittle instruction following.

This guidance starts to break down when teams try to use one rubric for every model task, because mixed-purpose evals usually hide the exact failure they are meant to expose.

Where Early LLM Evals Go Wrong and How to Adjust

Tighter evaluation criteria often increase review overhead, so teams need to balance speed against consistency. The most common mistake is to treat the first scoring rubric as permanent, when it should really be a draft that gets revised after reviewers see real disagreements. Another common failure is to overvalue aggregate scores while ignoring whether the scoring method matches the business task. A high score is not useful if it rewards fluent but incorrect outputs.

There is also a genuine tradeoff between breadth and depth. Broad coverage helps teams understand overall reliability, but deep review of a smaller set is what usually reveals why the system fails. Guidance here is not fully settled across the industry: some teams prefer task-specific scorecards, while others use shared quality dimensions across multiple use cases. The right answer depends on how similar the tasks are and how much reviewer consistency the organisation can sustain.

Teams should also watch for evaluation drift as prompts, retrieval sources, tool access, or policies change. If the system’s behaviour changes faster than the eval set, the benchmark stops representing production reality. That is why the best early evals are living artefacts, not static gates. For agent-heavy or tool-using systems, CSA MAESTRO agentic AI threat modeling framework is relevant when the eval needs to account for control-flow failure, not just answer quality.

Practitioner takeaway: the most reliable early signal comes from a small, real, and reviewable dataset that is updated as the system changes, not from a large benchmark that is hard to interpret.

Risk and Threat Considerations

LLM evals introduce governance risk when teams mistake a narrow benchmark for proof of real-world reliability. They also create operational risk if the evaluation set is too synthetic, too static, or too easy to game, because the model may look strong in testing while still failing on meaningful user tasks.

Failure mechanism: The risk materialises when the rubric measures the wrong property, the sample set misses important edge cases, or automated scoring becomes detached from human judgment. In those conditions, teams can optimise for the eval instead of the task, and model changes can quietly degrade real performance while the headline score stays stable.

Impact: The consequence is false confidence in deployment readiness, weaker detection of regressions, and slower response when the system starts producing unreliable, inconsistent, or unsafe outputs in production.

Practitioner Guidance

What to prioritise: Start with the most business-relevant failure mode, not the easiest metric to automate. If the system serves multiple use cases, evaluate the highest-risk task first because that is where rubric ambiguity matters most.

What to verify: Verify that human reviewers can apply the rubric consistently to the same sample set. If reviewer disagreement is high, the problem is usually the criteria, not the model, and the rubric needs revision before scale-up.

Common mistake: Do not optimise for benchmark size before you have confidence that the benchmark reflects production reality. Small, representative, and frequently refreshed often beats large and stale.

Practitioner takeaway: Treat evals as a calibration loop between task definition, human judgment, and model behaviour; if any one of those drifts, the score stops being trustworthy.

Related resources from NHI Mgmt Group

• What do teams get wrong about LLM-as-a-judge for agent evals?
• What do teams get wrong about filtering malicious prompts in LLM systems?
• What do teams get wrong about testing LLM-powered systems with traditional jailbreak prompts alone?
• What do teams get wrong when building clarification loops for AI agents?