How to Implement LLMs in Pharma

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The concept of generative AI and Large Language Models (LLMs) continues to grab headlines. Whether it’s the new Bedrock service or StableLM, LLMs are everywhere. For context, LLMs use deep learning techniques and data sets to understand, generate and predict content. Additionally, generative AI uses LLMs and machine learning to create human-like text, code or images by pulling from existing data.

While tools like ChatGPT have been popular industry-wide due to its groundbreaking capabilities, those in regulated industries should consider an alternative approach to their LLM strategy. In life sciences and drug introduction, scientific writing teams are working with confidential and sensitive patient data. As they exist today, leveraging open platforms is not a viable option. In this blog post, we’ll explore LLMs in pharma and what options biopharmaceuticals have when deploying LLM programs.

Taking a Human-Centric Approach to LLMs

When it comes to using the open web as a source of information, all companies, especially in pharma should proceed cautiously. Regulated industries, like those in life sciences, have a higher standard for content generation and there is zero margin for error. Further, the limitations of LLMs today for more regulated industries include: 

  • Potential for sensitive data to be exposed and concerns of public information leaking
  • No auditability, opaque models, and not fit for data-to-text automation 
  • Probabilistic by nature with zero logic layer for calculations 
  • Allows for creations, omissions, and hallucinations of vitally important information

Additionally, enterprise software for content generation should consider fundamental customer needs including, security, transparency and explainability of AI models, bias and fairness, as well as the auditability of results. With technical guardrails and specific domain training, implementing the right tools can be beneficial for first round drafts of regulatory documents. 

With that being said, LLMs in pharma should go beyond what existing probabilistic models are capable of. Solutions must come from a closed source, which is proprietary. Hybrid models are the key to success here as they blend the power of LLMs with customer data in a closed and secure environment.

Overall, taking a data and humanistic approach to implementing AI solutions is essential. Additionally, it’s critical to ensure the data being used is accurate and handled in a sensitive manner. It should go without saying that privacy shouldn’t be compromised. At Yseop, we ensure the data used to train systems is accurate, achieved ethically, and free of bias and hallucination. To avoid AI systems from perceiving data that doesn’t exist, the importance of this human-centered approach comes into play. It’s key for team members to be involved in this process to ensure false data is not generated.

Opportunities for LLMs in Pharma

Without a doubt, there are massive opportunities for LLMs in pharma. As the industry tries to keep up with increased demands, there’s a great need to deploy LLMs safely. Yseop’s platform brings Natural Language Generation (NLG) and other advanced technologies together to offer automation fit for regulated industries. To learn more about how Yseop can help your organization, please contact

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