Context as a Force Multiplier
The single variable that most differentiates expert AI users from novice AI users is not the sophistication of their prompts. It's the quality and completeness of the context they provide.
A novice user asks: "Write a summary of the key findings from this clinical trial."
An expert user provides: the target indication, the trial phase, the relevant comparator landscape, the primary audience for the summary (internal scientific review, regulatory submission, investor communication), the level of detail appropriate for that audience, the specific findings that are most important to highlight given the strategic context, and any considerations about how to frame the benefit-risk case given what the agency or the investor audience is likely to care about.
The AI's output is dramatically better from the expert's prompt — not because the AI is working harder, but because the AI has the information it needs to produce the right output.
The context components
Good context for biotech AI use typically includes:
Role and expertise level. "You are a regulatory affairs specialist with deep FDA experience." This calibrates vocabulary, depth, and frame of reference.
Purpose and audience. "This will be reviewed by the FDA's oncology division as part of a Type B meeting request." This shapes tone, level of technical detail, and what matters most to include.
Constraints and requirements. "This must comply with FDA's formatting requirements for Type B meeting packages and should not exceed 25 pages." This prevents the AI from producing output that's technically accurate but not practically usable.
Company and program context. "Our compound is a first-in-class KRAS G12C inhibitor in Phase 2. Prior FDA interactions have focused on the durability of response as the key question." This prevents the AI from producing generic output when specific output is required.
Prior work. "Here is our draft clinical strategy memo from last quarter. Maintain consistency with the framing and decisions in this document." This prevents contradictions with prior work and maintains the document trail.
The investment pays off
Investing 15 minutes in context building before a major AI task saves hours of revision. The output requires calibration rather than reconstruction. Expert context building is the highest-return skill in the AI user toolkit.