GMP Bench

Why General AI Benchmarks Fail for Regulated Industries

What general benchmarks actually measure

Benchmarks like MMLU, HellaSwag, ARC, and HumanEval were designed to evaluate broad cognitive capabilities: reasoning, common sense, reading comprehension, and code generation. They do this well. If you want to know whether Model A is generally “smarter” than Model B in an abstract sense, these benchmarks give you a rough signal.

But that's exactly the problem. They're designed to be general. The test items span dozens of domains (history, law, biology, physics, computer science,..) with a handful of questions in each. A model can score in the 90th percentile on MMLU without ever having been tested on a single GMP-relevant question. Note that most benchmarks also work based on multiple choice questions alone, no on task completion challenges like the GMP-benchmark.

For regulated industries, this creates a dangerous gap between perceived capability and actual fitness for purpose.

Why this matters more in regulated industries

In most knowledge work, a slightly wrong answer is an inconvenience. In regulated industries, it can be a compliance failure.

Consider what's at stake when AI is used in pharmaceutical manufacturing or quality management. An AI tool that confidently generates an incorrect interpretation of ICH Q10 guidelines doesn't just waste someone's time, it could lead to a flawed quality system, a failed inspection, or worse, a product quality issue that reaches patients. Regulated industries operate under frameworks where precision, traceability, and regulatory alignment aren't nice-to-haves. A model's ability to reason about common-sense physics or summarise Wikipedia articles tells you nothing about whether it can:

  • Correctly distinguish between a deviation and a CAPA
  • Draft an SOP that meets regulatory expectations for content and structure
  • Identify the appropriate response to an out-of-spec result
  • Navigate the nuances of Annex 1 contamination control requirements
  • Apply data integrity principles (ALCOA+) to a specific scenario

These tasks require domain knowledge, regulatory awareness, and an understanding of how quality systems actually work in practice. None of these are captured by general benchmarks.

What domain-specific evaluation looks like

Closing this gap requires benchmarks built by and for the people who actually work in regulated environments. Domain-specific evaluation differs from general benchmarks in several important ways.

Expert-authored test cases. Questions and tasks are written by subject matter experts, quality professionals, regulatory affairs specialists, validation engineers, not just scraped from the internet. This ensures the evaluation reflects real professional challenges, not textbook trivia.

Regulatory-aligned scoring.Responses aren't just graded on whether they're “correct” in a general sense. They're evaluated against regulatory expectations, industry standards, and the practical realities of compliance work. A technically accurate answer that would fail a regulatory inspection is not a good answer.

Task-based evaluation. Rather than multiple-choice questions alone, domain-specific benchmarks test models on realistic tasks: drafting documents, interpreting guidelines, reviewing procedures, and making risk-based decisions. These tasks mirror what professionals actually need AI to help with.

Nuance and context sensitivity.Regulated work is full of grey areas, situations where the “right” answer depends on context, risk assessment, and professional judgement. Good domain benchmarks capture this complexity rather than reducing everything to clear-cut right-or-wrong answers.

The current landscape

A handful of domain-specific benchmarks have emerged in fields like medicine (MedQA, PubMedQA) and law (LegalBench). These have proven valuable for those domains, showing that models which perform similarly on general benchmarks can diverge significantly when tested on specialised tasks.

But for pharmaceutical GMP, manufacturing quality, and regulatory compliance? The evaluation landscape has been essentially empty. Organisations adopting AI for quality operations have had to rely on ad-hoc internal testing, vendor claims, or (most commonly) gut feeling.

Building evaluation that actually helps

What regulated industries need is a benchmark framework that treats AI evaluation with the same rigor they apply to every other system they use. That means:

  • Transparencyabout what's being tested and how
  • Traceability from test cases to regulatory requirements and industry standards
  • Reproducibility so results can be verified and compared over time
  • Relevance to real tasks that professionals perform daily
  • Independence from model providers, so evaluation is unbiased

This is the thinking behind GMP Bench, a benchmark built specifically to evaluate AI performance on pharmaceutical GMP knowledge and tasks. Rather than trying to measure general intelligence, it focuses on whether a model can actually perform in the context where it would be deployed: helping quality professionals do compliance-critical work, accurately and reliably.

You can browse the test cases to see the types of evaluations included and how they map to real-world GMP requirements.