How To Staff LLM Evaluations At Scale

Automated metrics can tell you whether your large language model produces fluent text, but they cannot tell you whether the text is actually helpful, truthful, or safe. That judgment requires humans. The challenge is that human evaluation only works when the right people are doing the evaluating, with the right instructions, at the right scale. This guide walks through how to staff LLM evaluations from a small pilot to a production-grade program serving hundreds of evaluators across domains and languages.

Why Human Evaluation Is Non-Negotiable

Benchmarks like MMLU, HumanEval, and GSM8K have their place, but they measure narrow capabilities on static datasets. They cannot assess whether a model's response to a nuanced medical question is clinically appropriate, whether a legal summary omits a critical clause, or whether a creative writing output feels stilted. These are judgment calls that require domain knowledge, contextual reasoning, and an understanding of user intent.

Human evaluation fills this gap. It is the only reliable way to measure qualities like helpfulness, harmlessness, honesty, and instruction-following fidelity. If you are training with RLHF, your reward model is only as good as the preference data it was trained on, and that preference data comes from human evaluators. If you are running safety audits, your red team is a human team. The humans matter.

Defining Your Evaluation Rubric

Before you hire a single evaluator, you need a rubric that leaves as little room for interpretation as possible. Ambiguous rubrics are the single largest source of wasted effort in evaluation programs.

Rubric Design Principles

• Enumerate dimensions explicitly. Do not ask evaluators to rate "overall quality." Break it into axes: factual accuracy, completeness, relevance, clarity, safety, and instruction adherence. Each axis should have its own scale.
• Anchor every scale point with examples. A 5-point scale is meaningless without concrete examples of what a 1, 3, and 5 look like for each dimension. Include at least two examples per anchor point, drawn from real model outputs.
• Define boundary cases. What should an evaluator do when a response is factually correct but poorly formatted? When it is helpful but includes a minor hallucination? Document these trade-offs explicitly.
• Keep it short enough to memorize. If your rubric is longer than two pages, evaluators will stop consulting it. Distill the core decision rules into a one-page cheat sheet with links to the full document for edge cases.

Choosing a Scoring Format

The two dominant formats are pairwise comparison (A/B preference) and scalar scoring (Likert scales). Pairwise comparison is easier for evaluators and produces cleaner signal for reward model training, but it scales quadratically with the number of models you are comparing. Scalar scoring is more efficient for multi-model comparisons and gives you absolute quality measurements, but it requires heavier calibration to reduce rater drift.

For most RLHF workflows, start with pairwise comparison. For broad model benchmarking or regression testing, use scalar scoring with a well-calibrated evaluator pool.

Team Structure: Domain Experts vs. Generalists

Not every evaluation task requires a PhD. The right team composition depends on what you are evaluating.

When You Need Domain Experts

• Medical, legal, and financial content: Factual accuracy checks require subject-matter expertise. A generalist cannot reliably judge whether a model's explanation of drug interactions is correct.
• Code generation and review: Evaluators need to be proficient programmers in the target language. They must be able to run code mentally or in a sandbox and identify subtle bugs.
• Scientific reasoning: Math proofs, physics derivations, and chemistry explanations require evaluators with graduate-level training in the relevant field.

When Generalists Work

• Conversational quality and tone: Fluent speakers of the target language can assess whether a chatbot response sounds natural and helpful.
• Safety and policy compliance: With clear guidelines, trained generalists can flag content that violates safety policies. They do not need domain expertise to identify hate speech or personally identifiable information.
• Instruction following: Checking whether the model did what the prompt asked is a general skill, not a domain-specific one.

In practice, most programs run a hybrid team: a core of domain experts for accuracy-critical tasks, supplemented by trained generalists for volume-intensive work like safety screening and style evaluation.

Calibration: The Most Underinvested Phase

Calibration is the process of aligning evaluators so they apply the rubric consistently. It is the single highest-leverage activity in any evaluation program, and it is almost always underinvested.

Running Calibration Sessions

1. Gold set creation. Build a set of 50-100 tasks with "gold" labels agreed upon by your most experienced evaluators or internal team. These should span the full difficulty distribution, including edge cases and boundary examples.
2. Independent annotation. Have each new evaluator complete the gold set without seeing the reference labels.
3. Disagreement review. Walk through every disagreement in a live session. Do not just tell evaluators the right answer; explain why it is right by referencing specific rubric clauses. This builds shared mental models.
4. Iteration. After the session, have evaluators re-do a subset. Target at least 85% agreement with gold labels before they enter production.

Ongoing Calibration

Calibration is not a one-time event. Evaluator drift is real and measurable. Embed 5-10% gold tasks into every production batch. Track each evaluator's agreement with gold labels over time. When agreement drops below your threshold, trigger a recalibration session. Many teams run weekly 30-minute calibration meetings to review borderline cases from the previous week.

Scaling From 5 to 500 Evaluators

Scaling an evaluation program is not just about hiring more people. It requires changes to your infrastructure, quality assurance processes, and management structure.

Phase 1: Pilot (5-15 Evaluators)

Start with a small, highly supervised team. Use this phase to validate your rubric, estimate throughput per evaluator, and identify the most common sources of disagreement. Expect to revise your rubric at least twice during the pilot.

Phase 2: Growth (15-100 Evaluators)

Introduce team leads who manage cohorts of 10-15 evaluators. Team leads handle day-to-day questions, run calibration sessions, and escalate ambiguous cases. Implement automated quality checks: gold task agreement, time-per-task monitoring, and inter-annotator agreement sampling. At this stage, you also need a proper task routing system to balance workload and match evaluators to tasks based on their domain expertise and language skills.

Phase 3: Production Scale (100-500+ Evaluators)

At this scale, you need a program manager overseeing multiple team leads. Quality assurance becomes a dedicated function, not a side task. Implement stratified sampling for quality audits: check a fixed percentage of every evaluator's work each week, with higher sampling rates for new evaluators. Build dashboards tracking throughput, quality metrics, and cost per evaluation in real time.

Quality Assurance Mechanics

Inter-Annotator Agreement (IAA)

IAA measures how consistently different evaluators rate the same content. For pairwise comparison, track simple agreement rate (percentage of pairs where evaluators chose the same response) and Cohen's kappa to account for chance agreement. For scalar scoring, use Krippendorff's alpha, which handles ordinal scales and missing data.

Target benchmarks vary by task difficulty. For straightforward tasks (safety flagging, format checking), aim for kappa above 0.8. For subjective tasks (helpfulness, creativity), kappa above 0.6 is often realistic. If IAA is consistently below 0.5, your rubric needs work, not your evaluators.

Spot Checks and Adjudication

Assign a percentage of tasks to multiple evaluators (typically 10-20% for production batches). When evaluators disagree, route the task to an adjudicator, a senior evaluator or team lead who makes the final call and documents the reasoning. Adjudication decisions feed back into your rubric as clarifications and new examples.

Cost Modeling

Evaluation staffing costs depend heavily on domain complexity, language requirements, and whether you pay hourly or per task.

Hourly vs. Per-Task Compensation

Hourly pay works well for complex tasks where time-per-item varies significantly, such as evaluating long-form content or debugging code outputs. It reduces the incentive to rush through difficult items. Expect to pay $20-40/hour for generalists, $50-100/hour for domain experts in fields like medicine or law, and $60-120/hour for specialized code reviewers.

Per-task pay works better for standardized, well-defined tasks with consistent difficulty, like pairwise preference judgments on short outputs. It simplifies cost forecasting. Price per task based on median completion time from your pilot, with a buffer for harder items.

Total Program Cost

Beyond evaluator compensation, budget for: rubric development and iteration (40-80 hours of senior staff time), calibration sessions (2-4 hours per evaluator onboarding, plus ongoing weekly sessions), quality assurance infrastructure (tooling, dashboards, gold set maintenance), and program management overhead (typically 10-15% of evaluator costs at scale).

Tool Requirements

Your evaluation tooling needs to support several core capabilities: task routing and assignment, real-time quality dashboards, gold task injection and tracking, evaluator performance analytics, adjudication workflows for disagreements, and flexible rubric configuration without engineering support. Many teams start with spreadsheets or lightweight form builders, but these break down quickly past 20 evaluators. Purpose-built annotation platforms like Label Studio, Argilla, or Scale AI's tools handle the infrastructure layer. The critical decision is whether to build or buy the evaluator management layer on top.

How OpenTrain Helps

Building an evaluation team from scratch means sourcing candidates, vetting domain expertise, running calibration, and managing ongoing quality, all before a single production label is created. OpenTrain compresses this timeline by providing access to a pre-vetted network of over 100,000 AI trainers across 130 countries and 70+ languages. Every applicant completes an AI-powered interview that screens for domain knowledge, language proficiency, and decision quality. Teams can select evaluators by expertise, run calibration on the platform, and track quality metrics through a unified dashboard. For larger programs, OpenTrain offers managed services with dedicated program leads and throughput SLAs.

Putting It All Together

Staffing LLM evaluations at scale is an operational challenge as much as a technical one. The rubric defines what good looks like. Calibration ensures everyone agrees on what the rubric means. Quality assurance catches drift before it corrupts your data. And the right team structure lets you scale without sacrificing reliability.

Start small, invest heavily in calibration, instrument everything, and be prepared to revise your rubric as you learn. The organizations that treat evaluation staffing as a first-class engineering problem, rather than a procurement exercise, consistently produce better models.