How to Evaluate LLMs for Pharmaceutical Compliance Tasks

The two things you actually need to evaluate

GMP work has characteristics that general AI evaluation does not capture. LLM evaluation in a pharmaceutical context naturally separates into two distinct questions, and conflating them leads to poor decisions.

The first is whether the model knows the regulatory and scientific content. Can it correctly identify the requirements of 21 CFR Part 11 for electronic signatures? Does it understand the difference between parametric release under Annex 17 and standard batch certification under Annex 16? Does it know what “state of control” means in a CPV context? This is knowledge evaluation, and it can be tested relatively efficiently with well-designed question sets mapped to specific regulatory provisions and guidance documents.

The second is whether the model can actually do the work. Generating an environmental monitoring trending report from real-looking data. Drafting an SOP for a cleaning procedure that would pass a QA review. Writing a deviation investigation summary that correctly applies ALCOA principles, captures the relevant timeline, and identifies an appropriate CAPA. This is task evaluation, and it requires realistic prompts, relevant reference materials, and a scoring approach that reflects how quality professionals would assess the output (not whether it is grammatically acceptable, but whether it is regulatorily sound).

Both dimensions matter. A model with excellent regulatory knowledge that produces poorly structured documents, or one that writes fluently but misapplies guideline requirements, will create more problems than it solves. An LLM evaluation for GMP work should test both.

What task-based evaluation looks like

Task-based evaluation is where most informal assessments fall short. The typical approach is to give several models the same prompt, read the outputs, and pick a winner based on gut feel.

Rigorous task evaluation requires a rubric, a structured set of criteria against which each output can be assessed. For a deviation investigation summary, that rubric might include whether the timeline is internally consistent, whether the affected lot scope is correctly characterised, whether the root cause logic follows from the evidence, whether the proposed CAPA is specific and actionable, and whether the document meets the structural expectations of the organisation's QMS. Each dimension can be weighted, scored, and critically assessed by a domain expert rather than by someone whose expertise is AI.

The rubric makes the evaluation reproducible. Instead of “Model A seemed better,” you get “Model A scored 0.87 on regulatory accuracy and 0.72 on actionability; Model B scored 0.79 on regulatory accuracy and 0.84 on actionability.” That kind of structured comparison supports a defensible selection decision, which matters when you are documenting a supplier qualification or a GxP system selection.

Scoring task outputs at scale typically requires using a strong model as a judge, instructed to evaluate against the rubric dimensions. This is a well-established approach in AI evaluation, and it works effectively for pharmaceutical tasks when the judge is given sufficient regulatory context in its instructions and when its scores are periodically cross-checked against human expert review.

Dimensions a pharmaceutical LLM evaluation should cover

Across both knowledge and task evaluation, a useful evaluation framework for pharmaceutical compliance tasks should cover at minimum:

Regulatory accuracy. Does the model correctly represent the requirements of the applicable guidances? This includes not misattributing requirements (e.g., claiming Annex 11 requires something that is actually in Annex 22, or conflating FDA and EU GMP expectations), not omitting material requirements, and not introducing requirements that do not exist. This is arguably the most important dimension, because confidently wrong regulatory guidance is worse than no guidance at all.

Contextual appropriateness. Does the model apply the right regulatory framework for the context? An EU-based sterile manufacturing facility and a US-based API manufacturer have overlapping but distinct regulatory obligations. A model that defaults to a single regulatory lens regardless of context will produce outputs that require substantial correction.

Structural compliance. GMP documents have expected structures. A batch record, an SOP, a validation protocol, and a CAPA plan each have defined sections, sequencing logic, and content expectations that reflect both regulatory requirements and industry practice. A model that produces well-written but structurally incorrect documents creates rework, not efficiency.

Practical actionability.In pharmaceutical quality work, vague recommendations have limited value. A CAPA that recommends “improve operator training” without specifying what training, by whom, by when, and how effectiveness will be assessed is not a compliant CAPA. Evaluation should assess whether the model's outputs are specific enough to be acted on.

Appropriate conservatism. LLMs can be confidently wrong. In a GMP context, a model that hedges appropriately when facing regulatory ambiguity, or that flags when a question falls outside its reliable knowledge, is more valuable than one that generates authoritative-sounding text on everything. Evaluate how models handle genuinely ambiguous scenarios, not just clear-cut ones.

Building evaluation that is repeatable and defensible

One of the practical challenges in pharmaceutical AI adoption is that model evaluation is often treated as a one-time exercise. A vendor is assessed, a selection is made, and the evaluation is filed. This approach misses two important realities.

First, models change. A vendor may update the underlying model, modify the system prompt or guardrails, or change infrastructure components. Any of these can alter performance in ways that are not announced and not obvious. Evaluation needs to be repeatable, with a defined set of test cases that can be run against a new model version to detect meaningful performance changes.

Second, organisational needs evolve. As more use cases are brought into scope, the evaluation baseline needs to expand to cover them. A benchmark built around deviation drafting may not capture relevant capability differences when the organisation later wants to use the same tool for stability summary reporting.

The answer is a structured test library with version-controlled test cases, defined scoring criteria, and a cadence for re-evaluation when models or use cases change. This is not dramatically different from the periodic evaluation discipline that Annex 11 already requires for GxP computerised systems. It is the same principle applied to a more dynamic kind of system.

Where this fits in the broader validation picture

Selecting a model through rigorous evaluation is the start of the process, not the end. In GMP terms, evaluation feeds into the intended use definition and supplier qualification. The validated state is maintained through change control, version management, and periodic performance review.

This is also why evaluation and validation are complementary, not alternatives. A model can pass an evaluation and still require a validation package that covers the surrounding process: the prompt templates, the HITL workflow, the audit trail, and the change control procedure for model updates. Evaluation tells you which model is best suited to the task. Validation tells you that the system around it is fit for use in a GMP environment.

To get this right, treat LLM evaluation with the same rigour as any other system selection in the quality system, not as a technical curiosity, but as a documented, evidence-based process with defined criteria, expert involvement, and a clear link to the intended use.