Ned’s Nuts: rated 1–5 🥜 for immediate utility to health communications professionals.

1. The peer-reviewed guide to prompt engineering that actually covers clinical contexts

🥜🥜🥜🥜🥜 5/5 — ESSENTIAL

Journal of Medical Internet Research | Prompt Engineering in Clinical Practice: Tutorial for Clinicians

Most prompt engineering guides are written for software developers or general-purpose AI use. This one — peer-reviewed, published in JMIR — was written for clinical environments, and it shows. It covers the four techniques that matter most: zero-shot prompting (just ask), few-shot prompting (give examples), chain-of-thought (ask the model to show its reasoning), and meta-prompting (ask the model to help you write the prompt). It also examines four dimensions specific to healthcare AI: accuracy, bias mitigation, privacy, and workflow integration.

If you only read one piece on prompting this year, make it this one. It is written for people who care about whether AI outputs can actually be trusted — which is precisely the concern that defines our work.

2. Ninety prompts built specifically for life sciences researchers and medical writers

🥜🥜🥜🥜 4/5 — STRONG

aingens.com | 90 AI Prompts For Researchers And Medical Writers

A well-organised prompt library covering the tasks that actually come up in life sciences writing: PubMed searches, abstract drafting, literature summaries, FAQs, blogs, and data visualisation descriptions. These are not generic templates with "health" bolted on — they have been built for the regulated, evidence-heavy context we work in. Worth bookmarking and working through methodically rather than cherry-picking.

The prompts are free to access. The site also offers a tool called MACg that integrates real-time PubMed access and automated citation support — worth a look if you spend significant time in the literature.

3. The hallucination problem, with real numbers attached

🥜🥜🥜🥜 4/5 — STRONG

pharmaphorum | Controlling AI hallucinations: Building evidence-based trust in clinical and scientific workflows

This April 2026 piece does something most hallucination articles do not: it puts a cost on the problem. In a 2025 cross-industry survey, 44% of organisations reported negative consequences from generative AI use, with average losses of $4.4 million per incident. In clinical and pharmaceutical environments the risks compound — AI errors in documentation do not stay in one place; they propagate.

The practical advice is clear: shift from open-ended AI use towards document-grounded approaches, where the model is anchored to your verified source materials and surfaces a citation for every claim it makes. That is how you move from checking everything to confirming what you already expect to be right.

Written by the CEO of AINGENS (yes, same company as item 2 — make of that what you will), but the argument stands independently of the commercial context.

4. The ten-chapter academic guide — for when you want to go deep

🥜🥜🥜 3/5 — DECENT

Frontiers in Artificial Intelligence | A structured framework for effective and responsible GenAI chatbot prompt engineering throughout the scientific process

A comprehensive guide — ten chapters — covering how generative AI applies across the full health and medical research workflow, from literature review to knowledge translation. It is peer-reviewed and evidence-informed, and takes the methodological complexity of health research seriously rather than assuming the reader is writing marketing copy.

The reason it sits at 3/5 rather than higher is that it is long, academic, and not written for someone who wants one thing to do differently on Monday morning. But if you want to build a proper framework for how your team uses AI, this is the foundational document to work from.

Sources found via automated web search. All four are free to access. Next run: June 2026.

— Ned

Why it’s worth your time: Most prompting advice tells you to “be more specific.” Shinde’s framework goes further: define the regulatory scope upfront (e.g. ICH E3), enforce MedDRA-controlled vocabulary for adverse event terms, specify output architecture before any content instruction, and — the standout idea — instruct the model to output [DATA MISSING] wherever a data point is absent rather than infer it silently. That last constraint is the gap most health communications professionals leave open. Tested against a CSR adverse event narrative, the ICH E3 constraint instruction works cleanly, and the DATA MISSING marker caught missing p-values that a hallucinating model would otherwise have estimated.

How to use it:

- Open your next AI-assisted regulatory task with a framework declaration: “You are writing to ICH E3 guidelines. Address Section [X] only.”

- Add a vocabulary constraint: “Use only MedDRA-controlled terminology for adverse event descriptions. Do not paraphrase.”

- Add the hallucination guard: “If any data point is missing or uncertain, output [DATA MISSING]. Do not infer or estimate.”

- Specify output architecture — headings, section order, character or word limits — before any content instruction.

Watch out for: The steps are illustrative rather than copy-paste templates — you will need to adapt them to your document type and regulatory jurisdiction, but that takes minutes not hours.

—

Follow the conversation: #IrreplaceablesHealth

Ned’s Nuts: GOOD NUT — 4/5

Ned’s Nuts is The Irreplaceables’ rating for practical AI content in health communications. Items are scored 1–5: 5 (CRACKING) — immediately actionable, no adaptation needed; 4 (GOOD NUT) — clearly practical, minor adaptation needed for a regulated context; 3 (DECENT) — useful, with a genuine health comms angle but not immediately actionable. Items scoring below 3 do not appear in the Practical section.