🎯 Learning Objectives
- Condition AI to write in a human, non-generic voice
- Produce editorial and thought leadership content
- Adapt content for different audiences and platforms
- Write effectively for LinkedIn, whitepapers, and technical articles
- Avoid common AI writing patterns that signal inauthenticity
1. Human-Like Language Conditioning
Writing rules (strictly follow):
- Never use: "Furthermore", "Moreover", "It is important to note", "In conclusion"
- No passive voice. Every sentence has a clear actor.
- Lead every paragraph with the point, not the setup.
- One idea per sentence. Break long sentences ruthlessly.
- Contractions are allowed and preferred.
- No hedging: "It could be argued...", "One might consider..."
- Write as a confident expert speaks, not as they write a dissertation.
💡 Style Fingerprint
Paste 3–5 examples of writing you admire. Ask: "Analyse the stylistic patterns in these examples. Then apply those patterns to the following task."
2. Editorial & Thought Leadership Prompts
You are a senior pharma industry leader with 20 years of experience.
Write a thought leadership piece on: [Topic]
Rules:
- Open with a counterintuitive claim (not a question)
- Support with one specific example in the first 100 words
- Take a clear position — do not present "both sides"
- Include one original insight most people have missed
- Close with a specific call to action, not a vague reflection
Tone: Confident, direct, no hedging · Length: 500 words
3. LinkedIn, Whitepapers, and Technical Articles
LinkedIn Posts
Write a LinkedIn post about: [Topic]
- First line must create curiosity or tension (no "Excited to share...")
- Max 2 hashtags, placed at end
- Line breaks after every 1–2 sentences (LinkedIn formatting)
- Include one personal observation or story
- End with a question to drive comments
- 150–200 words maximum
Technical Whitepaper Executive Summary
Audience: Senior decision-makers with technical background.
Structure: Problem statement · Why existing solutions fail ·
Proposed approach · Evidence of effectiveness · Next step
Tone: Authoritative, evidence-based · Length: 250 words
4. Multi-Audience Adaptation
Take the following content: [Paste original]
Adapt for three audiences:
Version 1 — Board: Risk, ROI, strategic implication. 100 words.
Version 2 — Operations Manager: Practical impact, process changes required. 150 words.
Version 3 — Front-line Employee: What changes for them, why it matters. 100 words.
💡 The Same Facts, Three Stories
Each audience cares about different dimensions of the same reality. Multi-audience adaptation is relevance engineering — the facts stay the same; the framing changes completely.
✏️ Module 07 Exercise
Take a piece of content you've written recently. Run it through the human-language conditioning prompt. Then adapt it for two different audiences using the multi-audience template. Compare all three versions and identify what changed and why.
🔑 Key Takeaways
- AI writing patterns are detectable — explicit anti-pattern rules fix this
- Thought leadership requires a clear position, not a balanced survey
- LinkedIn has specific formatting constraints that must be explicitly specified
- Multi-audience adaptation is relevance engineering, not simplification
- Style anchoring with examples produces more consistent voice than describing style
- The best AI content leads with a bold claim, supports with specifics, ends with action