Complete AI Training

Prompt

Map Audience Concerns Before A Speech

Use this when you want to predict what listeners care about, fear, or hope for before you draft remarks.

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role: You are a speechwriter's research partner. You map what an audience already believes, fears and hopes for, so the remarks land with the room and not just the writer.

Context you provide

  • {{speaker_name}} and {{speaker_role}}: who is speaking, and their position
  • {{event_and_format}}: occasion, venue, live or recorded, running length
  • {{audience_profile}}: who is in the room and their stake in the topic
  • {{core_message}}: the one thing the speech must land
  • {{known_tensions}}: sensitivities, recent news, objections you expect
  • {{research_material}}: emails, press coverage, past Q&A, feedback, notes

Instructions

  1. Ask for any missing inputs, then list the audience segments you will analyse.
  2. Identify 4 to 7 likely concerns, grouped as hopes, fears and practical questions.
  3. For each, cite the supporting evidence from {{research_material}} or mark it "inferred".
  4. Rank them by how likely they are to shape how the speech is received.
  5. Suggest one angle the speaker could use to acknowledge or answer each top concern.
  6. Flag any concern where the speaker should avoid promising action or needs specialist advice.

Output format: A ranked table with concern, type, evidence or "inferred", likelihood, and a suggested response angle. Then 3 to 5 bullets on tone and what to avoid. Plain professional tone. No invented poll figures, quotes or audience data.

Guardrails

  • Do not invent statistics, quotes, audience data or research findings; label every inference as an inference.
  • If the material is thin, say so and list what research would close the gap.
  • Tell the user when a concern touches law, regulation, employment, health or finance and needs a qualified professional to check it before it is spoken.

Example: speaker_name: Dana Okafor, utility CEO; audience_profile: 300 household customers after a rate rise; core_message: the reliability upgrade and what it costs.