AI in Medical Affairs: Why context is the key to better AI output

📚 Why AI needs context to be useful

Large language models are trained on huge volumes of public human-written text.

That gives them broad language capability, but not an understanding of how your Medical Affairs team works. They do not automatically know:

• your internal standards and tone of voice
• your latest unpublished data
• your approved ways of structuring slide decks or documents
• your company-specific priorities, workflows, and expectations

That is why a vague first prompt often leads to disappointing output.

AI may sound fluent, but without the right context it cannot produce work that is truly aligned with your organisation.
 

🔄 The real shift for Medical Affairs teams

The lesson is that Medical Affairs professionals need to become better at briefing AI with clarity and precision.

That means being able to translate a task into:

• the right background
• the right scientific and business context
• the right constraints
• the right format for the intended audience

The better the brief, the better the result.

For teams working in a complex, regulated environment, this is not a minor prompt-writing trick. It is a practical capability that improves quality, relevance, and efficiency.
 

📋 A simple example

Imagine asking AI to create a slide deck for KOL engagement. 

A generic prompt will usually produce something superficial.

A better prompt would explain:

• who the audience is
• what therapeutic area or dataset is relevant
• what internal evidence or unpublished material should be reflected
• how your organisation typically structures these slides
• what tone, level of detail, and format are expected

That additional context is what turns AI into a useful working assistant.
 

✅ Practical takeaway: before you prompt, add context

Before using AI for a Medical Affairs task, ask:

• What does the model need to know that is specific to our company?
• What background or data is missing from the original request?
• What would I tell a new team member before asking them to do this task?
• What should the final output look like?

These questions often make the difference between a weak first draft and a genuinely useful result.