In March, OpenAI announced the release of the Fine-Tuning API (Application Programming Interface), specifically designed to facilitate the fine-tuning process for language models. The new availability of the Open AI Fine-Tuning API makes using generative AI in commercial operations an imminent reality versus a theoretical prospect.
The Fine-Tuning API enables developers to adapt pretrained models to specific tasks or domains, providing more flexibility and customization.
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In addition to OpenAI, various other platforms and frameworks, such as Hugging Face, Google’s TensorFlow, and Microsoft’s Azure Machine Learning, have incorporated fine-tuning as well as prompt engineering capabilities into their Natural Language Processing (NLP) offerings. By harnessing the capabilities of these techniques, life sciences organizations can gain a competitive edge, accelerate decision-making, and enhance customer engagement.
Fine-tuning enhances content development
Fine-tuning takes a pre-trained language model, such as GPT-3.5, and adapts it to perform specific tasks or understand domain-specific information.
For pharmaceutical commercial operations, fine-tuning can be used to create hyper-personalized content to support brand managers, customer experience professionals, sales teams, and medical science liaisons, and alleviate some of the heavy lifting it has traditionally taken to create, organize, and analyze customized content.
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In addition, fine-tuned models can scour the web and social media platforms to gather information about competitors, industry trends, and market dynamics. By monitoring relevant conversations, pharmaceutical companies can stay current with the latest developments, identify new opportunities, and make informed strategic decisions.
For example, with fine-tuning, generative AI can nearly instantaneously create initial drafts of copy for a variety of customer channels. While this content will still require editing and refinement by marketing teams, as well as the required medical/legal/regulatory review, AI can help to expedite content development and empower marketing teams to become more efficient and focused.
Another capability that is now possible through fine-tuning is refined content tagging. Content tagging has always been a tremendously laborious task. Auto-tagging helps the right content get delivered to the appropriate customers. Generative AI has already proven its ability to automate content tagging, which saves creators tremendous time and effort, as well as reduces the possibility of mistakes.
Here’s an example: Let’s take approved email templates and key messages as our specific marketing content. Fine-tuning models can target specific domains and generate prompts to tag the content into topics. These content topics can then be used to generate message “sets” that contain information relevant for a specific channel and topic. These message sets can then be used to support marketing campaigns, generating specific content recommendations based on a topic.
Prompt engineering delivers deeper HCP insights
Prompt engineering can also play an important role in pharmaceutical commercialization by enabling companies to leverage AI models for various operations. By carefully designing prompts, companies can utilize AI models to transcribe and summarize interactions between representatives and healthcare professionals, providing valuable insights and analytics. This helps in managing customer relationships by capturing preferences, interests, concerns, and action items more efficiently.
Additionally, prompt engineering serves as a companion to fine-tuning, optimizing model performance through the thoughtful formulation of prompts. It guides the fine-tuning process, enhancing the model’s output and aligning it with specific objectives.
With prompt engineering, pharmaceutical sales teams can benefit from personalized recommendations and richer insights. For instance, by using prompts like “Identify key opinion leaders in the field of lung cancer,” AI models extract relevant information to support targeted engagement strategies. This leads to more effective marketing campaigns and higher sales conversion rates while also improving the customer experience for doctors. As a result, too, medical science liaisons and individual sales representatives can develop closer, long-term relationships with top physicians.
Generative AI further enhances the value of prompt engineering by synthesizing important insights from various interactions, including text, images, compliant transcripts, and call notes. By automating this process, generative AI quickly transforms on-the-ground insights into robust analytics. This improves Next Best Action (NBA) recommendations from intelligence engines, leading to stronger and more meaningful relationships with healthcare professionals.
Here’s an example: A prompt like “summarize key discussion points and action items from the meeting with Dr. Jones” can guide the AI model to extract relevant information and generate a concise summary of the meeting. The summary can highlight important topics discussed, any action items agreed upon, and specific areas of interest expressed by Dr. Jones.
With prompt engineering, commercial and medical teams get accurate and organized information about their interactions with HCPs in near real-time. This empowers them to follow up with personalized and targeted communication, providing relevant information and resources that address the specific needs and interests of each HCP.
In addition, prompt engineering can also support the analysis of a large volume of interactions, allowing AI models to identify patterns and trends across multiple HCP engagements. By formulating prompts that capture specific metrics or parameters, such as “analyze prescribing patterns among HCPs in a specific region,” the AI model can extract data and generate insights that help identify opportunities for improved engagement and tailored support.
A new era of generative AI is here
Fine-tuning and prompt engineering have ushered in a new era of generative AI advantages for commercial operations at pharmaceutical companies. Supporting APIs and application frameworks make it possible to fully leverage these novel techniques and provide an immediate and concrete way for teams to benefit from generative AI including optimizing sales and marketing efforts, developing better relationships with healthcare professionals, and capturing a deeper understanding of customers.
As the field of AI continues to advance, further advancements in fine-tuning and prompt engineering are anticipated. They also offer a glimpse into the long-term transformative capabilities of generative AI. With continued exploration and integration of these techniques, commercial pharma operations can unlock even greater value, accelerate innovation, and navigate the evolving landscape with confidence.
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As co-founder of Aktana, Derek Choy spent the last decade developing and refining Aktana’s AI-based product, evangelizing the company vision in the life sciences industry, and delivering solutions to customers across four continents to establish Aktana as the global leader in decision support for life sciences. As chief operating officer, Derek is responsible for strategic definition and scalable execution of Aktana’s vision. Prior to Aktana, Derek held various roles in the technology and services sector. He drove initiatives at Intuit, developed and implemented strategies for a diverse range of public and private sector clients at The Boston Consulting Group, worked at several large law firms in Australia and Hong Kong, and co-founded a medical IT company in Australia. Derek has dual degrees in computer science and law (honors) from the University of Melbourne.
