Category: Generative AI & LLMs
General-purpose language models can produce fluent text containing fabricated numbers, citations, or claims — a failure mode regulated pharma documents cannot absorb.
The most discussed failure mode of large language models — confidently generating plausible but false statements — is a manageable annoyance in many industries and a disqualifying risk in regulated pharma documents, where a fabricated number or citation can reach a regulator, a site, or a patient. Teams that want generative AI's drafting speed need architectures and controls that constrain what a model can claim. This is a solvable engineering and process problem, and it is the central evaluation criterion that separates tools built for this industry from general-purpose ones.
The techniques that make generative AI usable in regulated content share one principle: the model is constrained to work from retrieved, verifiable source material rather than from its open-ended training knowledge. Retrieval-grounded architectures fetch the relevant approved documents, tables, or database records first and instruct the model to draft only from them; citation enforcement requires each generated claim to point at its source; and templated generation limits free composition in the passages where precision matters most. Layered on top, verification passes — automated cross-checks of numbers against source tables, and mandatory human review — catch what constraint misses. None of this reduces the human role: it makes human review tractable. A reviewer facing a draft where every claim links to its source can verify systematically; a reviewer facing fluent unsourced prose can only hope to spot the fabrication. The architecture determines which review situation your team is in.
Ask a vendor to show, concretely, what happens when the source material does not contain the answer — the honest behaviors are an explicit refusal, a flagged gap, or a request for input, and the disqualifying behavior is fluent text that fills the void. Test with your own documents, including deliberately incomplete ones. Ask how numbers are handled specifically, since numeric fabrication is both the most damaging and the most detectable failure: strong tools copy or compute numbers from source data rather than generating them as text. Then look at the review workflow: whether claim-to-source links survive into the reviewing environment, whether reviewer corrections feed back into the system, and what the audit trail records about what was generated, from which sources, under which model version — the evidence your quality organization will need.
A practical evaluation is adversarial: assemble a test set from your own documents where the ground truth is known, remove some source material deliberately, and measure how the tool behaves at the gaps. Score fabrication rate, refusal behavior, and citation accuracy separately — a tool can be strong on one and weak on another. Teams typically start production use in draft-only mode inside a workflow where human review is already mandatory, so the controls are proven against real work before anyone depends on them.
No system should be sold or bought on that promise. The realistic goal is layered defense: grounded generation that constrains what the model can claim, automated verification of numbers and citations against sources, and mandatory human review as the final gate. The question to ask is not whether hallucination is impossible but whether the controls make fabrication rare, detectable, and caught before release.
Domain adaptation helps with terminology and convention, but grounding architecture matters more than model pedigree: a general model constrained to retrieved sources with citation enforcement is typically safer for regulated content than a domain model generating freely. Evaluate the system's controls, not just the model inside it.
Give the tool a task where the source material is missing a needed fact, and watch what it does. An honest system flags the gap or refuses; an unsafe one writes plausible text anyway. Run this test on your own documents rather than the vendor's curated examples.
Only if the review is genuinely feasible. Review catches fabrication reliably when generated claims are linked to their sources and reviewers can verify each one; it becomes a false comfort when reviewers face fluent unsourced text under deadline pressure. The tooling's job is to make review systematic rather than heroic.
The U.S. FDA renewed and expanded its long-standing collaboration licensing Certara's Simcyp physiologically-based pharmacokinetic (PBPK) simulator, with hundreds of agency licenses used to inform regulatory review of drug dosing and interactions.
Vendor: Certara · press release (Certara (GlobeNewswire)) · 2021-12-21 — Partner: U.S. FDA
Yseop announced a strategic investment and a milestone of clinical trials supported by its generative AI for regulatory and medical writing.
Vendor: Yseop · press release (Yseop (GlobeNewswire)) · 2023-12-07