Why Fine Tuning is Not Fine for Pharma Marketing

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What is Fine-Tuning and How Does It Work?

In pharmaceutical marketing, adherence to PRC/MLR-approved content is non-negotiable. Fine-tuning, a method of adapting large language models (LLMs) for specific tasks, might seem like an ideal solution. It’s akin to training a generalist to become an expert, but even with fine-tuning, ensuring absolute consistency with PRC/MLR-approved language poses challenges.

Fine-tuning starts with a general-purpose model, trained on a broad dataset to understand language, context, and structure. The process then refines this model using task-specific data—in this case, content explicitly approved by PRC/MLR. By comparing its outputs against the approved text and adjusting its parameters, the model learns to prioritize the desired content style.

This approach is efficient for scaling content delivery, enabling faster generation of materials like patient education brochures or sales training guides. But fine-tuning has limitations. Even with rigorous adjustments, fine-tuned models generate probabilistic outputs, which means they are highly likely, but not guaranteed, to align with PRC/MLR-approved content. This lack of determinism creates risks for pharmaceutical marketing, where even minor deviations from approved content and messaging can lead to compliance breaches.

What is Training and How Does It Work?

Training is the foundation for how language models understand text. In LLMs, training involves processing massive datasets to predict word sequences, developing a nuanced understanding of language patterns. However, in many pharmaceutical contexts, this general-purpose understanding is insufficient—models are constrained by highly specific and regulated content.

Unlike fine-tuning, training creates broad capabilities, not specialized outputs. For pharmaceutical marketing, this means a model trained on general data might interpret “common side effects” or “indication information” inappropriately unless fine-tuned or governed by strict rules. The distinction is critical: while training provides the foundational knowledge, other interventions are necessary to align outputs with PRC/MLR-approved language and ensure a compliant end product.

Ultimately, neither training nor fine-tuning fully guarantees adherence. This is because models inherently predict based on probabilities, not fixed rules—a fundamental challenge when working in regulated industries.

The Difficulty with Guaranteeing the Same Answer

Pharmaceutical marketing demands absolute precision in every piece of collateral that is presented to an end user, from doctor discussion guides for prospective patients to summaries of clinical trial data for healthcare professionals (HCPs). PRC/MLR-approved content must be presented verbatim, with the appropriate context and without paraphrasing or hallucination. Yet, fine-tuned LLMs struggle to guarantee consistent reproduction of specific phrasing.

For example, a fine-tuned model generating responses for a medication’s indication might produce slight variations if prompted differently: "This drug is approved for X" versus "This medication may treat X." While acceptable in other industries, this kind of variability can have serious compliance implications in pharma, and will rapidly undermine trust in the suitability of the model’s use in pharmaceutical applications.

This inconsistency stems from how LLMs work. Each response is probabilistic, based on the likelihood of word sequences. Even small variations in the input can shift the model’s probabilities and result in different and thus non-compliant outputs.

High Probability ≠ Certainty

Fine-tuned models might appear reliable, producing accurate outputs in most scenarios. But "most scenarios" is insufficient for pharmaceutical marketing. A single deviation from PRC/MLR-approved language can lead to regulatory scrutiny and fines, and damage patient and HCP trust in the brand as a result.

Consider a fine-tuned model trained on FDA-approved drug labels. While it might frequently generate compliant outputs, there’s no guarantee it won’t occasionally introduce unapproved language; or even take approved language but present it in an inappropriate and unapproved context. High-probability outputs cannot replace deterministic processes when every word must be right, every time.

In pharma, compliance is binary—materials are either compliant or they’re not. There’s no middle ground.

When It Has to Be Right 100% of the Time

For pharmaceutical marketing, the stakes are uniquely high. A sales representative misquoting drug safety information or a consumer-facing chatbot providing inconsistent responses can lead to severe regulatory and reputational consequences. In these contexts, the use of generative AI models, even fine-tuned ones, are inherently limited.

To address these challenges, deterministic systems must supplement or replace probabilistic models in high-stakes applications. These systems work by enforcing strict adherence to PRC/MLR-approved content, utilizing ironclad guardrails to prevent errors. For example:

• A rule-based framework could check every model output against a database of approved content, rejecting anything that is not in the database as non-compliant.
• Post-processing layers could evaluate outputs for verbatim accuracy before distribution.
• Hybrid approaches could pair LLMs with deterministic mechanisms, ensuring flexibility for user queries while maintaining strict compliance.

These approaches can help pharmaceutical organizations leverage the benefits of LLMs—like scalability and natural language understanding—without compromising on regulatory requirements.

Addressing Uncertainty in Pharma AI Strategy

Pharmaceutical marketers have long been asked to strike a delicate balance between both compliance and creativity in their work. And that balancing act is only becoming trickier as AI is added to the mix. Before integrating AI technologies into pharmaceutical marketing workflows, business owners and brand managers must ask critical questions, such as:

• Does the task require verbatim reproduction of PRC/MLR-approved content?
• What safeguards exist to ensure compliance in every output?
• Are deterministic systems or human oversight incorporated to manage risk?

AI can transform pharmaceutical marketing by scaling personalized content development and delivery and optimizing processes. But it must be deployed responsibly, with clear guardrails to ensure compliance. Traditional tactics like training and fine-tuning are, on their own, not the answer. By combining LLM capabilities with deterministic systems and rigorous oversight, pharma companies can embrace innovation without risking the precision and consistency that compliance demands.

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Ray Gilbert is the VP of Engineering at Ostro. Before joining Ostro, he served as an engineering leader at Amazon, contributing to the development, growth, and scaling of the award-winning Ring Alarm, a life-saving product with millions of users. In addition, he holds multiple patents, exemplifying his commitment to innovation and technical excellence. With extensive professional experience spanning two decades in engineering leadership, Ray has guided cross-functional teams to deliver impactful solutions across diverse industries.

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