Core principle
The quality of AI-assisted writing is set before the first draft appears — by how well you brief the model. And it is protected after the draft appears — by how rigorously you evaluate it. These two skills, Description and Discernment, form a loop: brief, draft, evaluate, refine, and feed what you learn back into the next brief. Most of this playbook covers the discernment half — verification, compliance screening, review, and sign-off. This page covers the half that gets less attention in regulated writing: the brief. A vague brief produces a generic draft, and a generic draft costs more review time than a good brief would have cost up front.This page applies concepts from the AI Fluency Framework developed by Prof. Rick Dakan (Ringling College of Art and Design) and Prof. Joseph Feller (University College Cork), as taught in Anthropic’s AI Fluency courses. The framework describes four competencies — Delegation, Description, Discernment, and Diligence. Description and Discernment are the two with the most direct application to day-to-day medical writing; the playbook’s risk tiers and review protocols map broadly to Delegation and Diligence.
Description: brief AI like you’d brief a writer
If you handed a deliverable to a freelance medical writer, you would never say “write a manuscript about this trial” and walk away. You would send the brief, the data, the journal requirements, examples of prior work, and the client’s preferences. AI needs the same brief — and unlike the freelancer, it cannot ask around the office for what you forgot to include.Provide examples of approved work
The fastest way to convey voice, structure, and register is to show, not describe. Where your organisation’s policies permit, include in your prompt context:- A previously approved deliverable of the same type (an approved PLS, a past congress summary, a signed-off slide deck)
- The house or client style guide, or the relevant extract
- An approved messaging framework or claims matrix, where one exists
- The target journal’s author guidelines, congress abstract rules, or channel specifications
Inject what AI cannot know
AI knows the published literature up to its training cutoff. It does not know:
The single biggest upgrade to most prompts is not cleverer instructions — it is adding the two or three facts the model had no way of knowing.
Describe the approach, not just the output
Beyond what to produce, describe how to produce it: “work only from the attached sources; flag anything you cannot support rather than filling gaps” is an approach instruction, and it is the practical implementation of source grounding. The prompt patterns in this playbook are structured this way — output format, source constraints, and behavioural rules stated explicitly.Discernment: evaluate more than accuracy
The playbook’s review checklists cover accuracy, completeness, and compliance in depth. Two further discernment questions are easy to skip because nothing “fails” when they go unasked:Does it sound like us?
AI defaults to a recognisable register — fluent, balanced, slightly padded, fond of “moreover” and tidy triplets. For an internal summary, that may not matter. For a client deliverable, an authored manuscript, or patient-facing content, it does. Ask explicitly:- Would a reader familiar with our (or the client’s) content recognise this as ours?
- Does the phrasing match the approved lexicon — or has the model substituted near-synonyms that were deliberately avoided?
- Is anything padded, hedged, or generically “professional” where the house style is direct?
Is the process working — not just the output?
Discernment applies to the collaboration itself, not only the draft in front of you. If every draft needs the same correction, the fix belongs in the brief, not in another round of edits. Signals worth acting on:- You keep re-explaining the same context → move it into a standing briefing block
- The model keeps overreaching the sources → tighten the grounding rules in the prompt
- Drafts are accurate but tonally wrong → add or improve the voice example
Closing the loop: revise with specifics
Vague feedback to AI produces vague revisions — the model guesses at what you meant, and its guess is usually “more adjectives.” Specific feedback produces controlled revisions.
Two rules keep iteration safe in medical writing:
- Pin what must not change. When asking for a revision, state explicitly which sections, data points, and qualifiers must be preserved. Unconstrained revisions can quietly re-word verified content — which then needs re-verification.
- Re-verify what changed. A revised draft is a new draft. Anything the model touched goes back through the applicable checks before sign-off.
Build a briefing pack, not a prompt
The course framing of a “cognitive environment” — context that accumulates across a collaboration — has a practical med comms translation: a reusable briefing pack per client, product, or project. A living document containing:- Approved voice examples and style rules
- The standing context block (product, indication, audience definitions, off-limits territory)
- Grounding and behavioural rules that have proven necessary
- A running list of corrections that recur — each one a candidate for promotion into the standing brief
How this maps to the playbook
Concepts adapted from the AI Fluency Framework by Rick Dakan and Joseph Feller, as taught in Anthropic’s AI Fluency courses (course materials © 2025 Anthropic and GivingTuesday, CC BY-NC-SA 4.0). The application to medical writing is the playbook’s own.
Last reviewed: 26 July 2026 · 7 min read