How to evaluate AI models for your company: A step-by-step guide

What is model evaluation?

Model evaluation is the process of deciding whether an artificial intelligence (AI) model is a good fit for a specific use case, level of risk, and work environment. It helps organizations choose whether to adopt, reject, limit, or continue testing a model based on measured performance. Mercor’s guide on what AI evaluation is provides a broader overview.

AI testing measures how a model performs under specific conditions, while AI model evaluation uses those results to determine whether the model meets an organization's requirements and fits the intended workflow. Put simply, testing produces evidence, while evaluation uses that evidence to support a decision.

This distinction is important because even a highly capable model can be a poor fit for a specific job. A model that writes fluent contract summaries, for example, may be unsuitable for legal review if it misses important termination rights or requires so much oversight by a lawyer that it doesn't save time or money.

Key AI model evaluation metrics

AI model evaluation relies on more than one overall performance score. Teams should consider quality, reliability, safety, cost, speed, domain fit, and other requirements that determine whether a model can support the intended workflow. Using a scorecard can make it easier to compare those requirements and the supporting evidence when making a final decision.

The core factors to evaluate include:

• Quality and correctness: Does the output meet the factual and professional standards for the task?
• Reliability and consistency: Does performance remain stable across repeated runs, different inputs, and difficult cases?
• Safety: Does the model avoid harmful behavior without refusing valid requests too often?
• Total cost: What will the model, human review, integration, monitoring, corrections, and future changes cost?
• Latency: Is the model fast enough for the workflow?
• Domain-specific performance: Does the output meet professional standards?
• Security, compliance, and deployment fit: Can the model meet required data, regulatory, technical, and integration requirements?

Specific metrics can provide evidence for these criteria. For example, precision measures how often positive predictions are correct, recall measures how often the model finds the relevant positive cases, and F1 balances the two. Generative AI tasks may rely more on measures such as pass rates, groundedness, or expert rubric scores.

The best metrics to measure depend on the work being evaluated. For contract extraction, recall can show how often the model misses required clauses, while an expert rubric can determine whether an open-ended risk explanation is legally useful.

Other requirements may rule out a model regardless of its performance score. For example, a model that can't meet required security, privacy, compliance, or deployment standards may be unsuitable even if it produces the highest-quality outputs. Teams should therefore define their minimum requirements and acceptable error levels before comparing candidates.

How to evaluate AI models in 5-steps

The best way to evaluate AI models is to define the workflow the model will be used for, gather examples that accurately reflect your work, choose the right evaluation methods, set performance thresholds, and compare the remaining trade-offs. Each stage provides supporting evidence to help teams determine whether a model is a good fit for their work.

Step 1: Define your use case and evaluation criteria

Describe the workflow the model will support: who will use it, what inputs it receives, what outputs it must produce, where human oversight is required, and what failures could cost the organization. Then identify any regulatory, security, deployment, and service requirements. Separate requirements the model must meet from preferences that can be balanced against other factors.

For contract review, for example, protecting client data and identifying every high-risk clause may be mandatory, while drafting style may matter less. Evaluate each model against the specific workflow rather than assuming one candidate will perform equally well across legal, finance, and engineering tasks.

Step 2: Build your golden dataset

A golden dataset is a compact, representative set of approved examples and edge cases that reflects your work and the standards experts in your field expect. It should include routine tasks, costly failure cases, meaningful input variation, and judgments from people who are qualified to define a good answer. A contract-review set, for example, should contain both standard agreements and unusual clauses that could create significant risk.

Keep this dataset separate from model configuration and tuning so it remains a fair test of performance. The National Institute of Standards and Technology (NIST) follows a similar principle in its Artificial Intelligence Technology Evaluation program, which uses sequestered evaluation data to reduce train-test contamination. The same approach helps prevent internal evaluations from becoming rehearsed demonstrations.

Step 3: Choose the right metrics and evaluation methods

Match each criterion with a measurable evaluation method. Structured tasks may rely on automated checks, while open-ended professional work often benefits from expert rubrics or human review. Safety, usability, cost, and speed may require security testing, user studies, or measurements from real workflows.

Expert review is especially valuable when several answers seem reasonable but only a specialist can identify an important error. Public AI model benchmarks can provide evidence of broader capability, while private workflow evidence shows whether a model fits your organization’s needs. AI evaluation tools can organize the evidence, but they can't decide which mistakes your business can tolerate.

Step 4: Compare your results to performance thresholds

Set minimum performance thresholds and useful baselines before ranking candidates. A model should meet all of your organization's essential requirements before you compare its overall performance with other options. Record any uncertainty, gaps in the evidence, and the conditions under which each result was measured.

A candidate may lead an AI benchmark ranking and still fail a required privacy standard, exceed the speed limit, or miss critical clauses too often. One overall score can hide those problems, so it's important to set minimum requirements first. This approach also aligns with the NIST AI Risk Management Framework, which gives organizations a voluntary structure for managing AI risk without setting one universal performance standard.

Step 5: Consider performance, costs, risks, and trade-offs

Eliminate candidates that fail essential requirements, then compare the remaining options across the factors that matter most. Consider the full cost of using each model, including model usage, human review, integration, monitoring, corrections, and switching costs. Document why the chosen model is the best fit, which trade-offs the team accepted, and any changes that would trigger a new evaluation.

A smaller or less expensive model may be the best choice if it meets every required threshold and is easier to operate.

AI model evaluation best practices

A good evaluation process should make it clear why a team chose one model over another. These practices help you make consistent, well-documented decisions that are easy to review later:

• Set your criteria and minimum requirements before reviewing vendor evidence or internal results.
• Use representative company work that has not been used to configure or tune the model.
• Include domain, product, security, legal or compliance, operations, and technical experts in the evaluation process.
• Compare candidates under equivalent conditions, including model version, tools, context, and testing assumptions.
• Combine evidence from AI benchmarks, expert review, automated checks, and real-world performance measurements.
• Document uncertainty and trade-offs instead of reducing everything to one overall score.
• Assign an owner and review date so the decision can be revisited when models, prices, workflows, or requirements change.

Be wary of vendor-selected examples, unclear model versions, a single headline score, and benchmarks that don't resemble the intended task. Public benchmarks can strengthen a comparison, but they shouldn't replace testing against your own requirements.

When evaluating AI models for a specific domain, it's helpful to consider relevant benchmarks. Mercor’s APEX-Agents benchmarks cover real-world professional work, while APEX-SWE includes software engineering benchmarks for integration and observability tasks. Teams can also compare AI models for software engineering while still validating candidates against their own tools, workflows, and requirements.

How does Mercor evaluate AI models?

Mercor evaluates AI models using work that reflects the professional tasks organizations need completed. The process defines the inputs, expected outputs, limits, and success criteria for each type of work. Experts help establish what good performance looks like, while automated and human review provide evidence at the right scale.

Real-world model evaluation

Representative tasks include the parts of a workflow that can affect performance, such as realistic inputs, expected deliverables, relevant context, professional requirements, and task difficulty. The goal is not to recreate an entire production environment but to test the conditions that matter most for the work the model will perform.

A realistic financial analysis or software incident task, for example, can provide more useful evidence than a general capability score when an organization is choosing a model for that type of work.

Expert and automated performance assessment

Mercor combines expert-created rubrics and clear scoring criteria with automated checks and expert human review. Automation can efficiently verify structured requirements, while qualified experts can judge complex work, professional quality, and difficult edge cases.

Experts also help check automated scoring and resolve disagreements. Their involvement helps prevent model-based evaluation from being treated as the final authority when professional judgment is still required.

Evaluation results for model selection

Evaluation results allow teams to compare candidates, identify recurring weaknesses, determine whether minimum quality standards are met, and show where more testing or improvement is needed. This helps teams make a decision rather than simply produce a score.

Performance is only part of the decision. Cost, speed, reliability, safety, deployment requirements, and workflow fit are just as important. As models or requirements change, teams can use the same approach to reevaluate their choices. Domain-expert evaluation data can build evidence around the work and standards that matter to your business.