Adapting to personas and audiences

Adapting to personas and audiences

Why it matters: Persona adaptation increases relevance without changing core meaning.

Goals

• Define 2 personas.
• Adapt tone/format per persona.
• Keep facts consistent.

Key definitions

• Persona: A target reader with needs, context, and constraints.
• Audience constraint: A limitation like attention span, expertise, or role.
• Core meaning: Facts, decisions, and commitments that must not change.

Workflow (step-by-step)

1. Define persona A and persona B (role, goals, fears).
2. Define the same core message (facts/commitments).
3. Rewrite for each persona (tone + format + examples).
4. Run a meaning diff to confirm no drift.
5. Ask for a final QA against style and meaning.

Example (good vs bad)

✅ Good: Two versions that keep facts consistent but feel tailored.

❌ Bad: Persona versions that change claims or add invented details.

Checklist

• Personas defined.
• Core meaning extracted.
• Meaning diff checked.
• Tone matches persona.

Metrics / criteria

• Meaning drift 0; engagement improves.
• Measurable: You can say whether the output is correct/complete (pass/fail or a score).
• Effort: You can produce the result in 15–30 minutes using the workflow.
• Clarity: Another person can run your prompt and get a similar outcome.

Common mistakes + fixes

• Pitfall: Over-personalization that changes meaning. Fix: Lock facts/commitments before rewriting.
• Pitfall: Vague instructions. Fix: Add constraints, examples, and a success checklist.
• Pitfall: One-shot prompting. Fix: Iterate: draft → critique → revise → verify.
• Pitfall: No validation. Fix: Add QA steps, tests, and explicit “what counts as done”.

Student tasks

1. Apply the workflow to one real work task related to adapting to personas and audiences.
2. Create a small artifact: prompt, checklist, rubric, table, draft, or decision note.
3. Run one quality check and record what changed after the check.
4. Write a 3-sentence reflection: what worked, what failed, and what you will reuse.

Useful external sources

• OpenAI prompt engineering guide: https://platform.openai.com/docs/guides/prompt-engineering — Prompt structure, iteration, constraints, and evaluation habits.
• NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework — Risk framing, governance language, and verification mindset.
• OWASP Top 10 for Large Language Model Applications: https://owasp.org/www-project-top-10-for-large-language-model-applications/ — Practical AI safety risks such as prompt injection, leakage, and insecure output handling.
• Google People + AI Guidebook: https://pair.withgoogle.com/guidebook/ — Human-centered AI design, user control, feedback, and failure modes.

Bibliography

• OpenAI prompt engineering guide. https://platform.openai.com/docs/guides/prompt-engineering
• NIST AI Risk Management Framework. https://www.nist.gov/itl/ai-risk-management-framework
• OWASP Top 10 for Large Language Model Applications. https://owasp.org/www-project-top-10-for-large-language-model-applications/
• Google People + AI Guidebook. https://pair.withgoogle.com/guidebook/