Turn your talks
into delivery you can coach.

A different approach to prompts for speakers.

An AI prompt for speakers is an instruction that turns a raw recording, talk, interview, or round of audience feedback into a structured answer: what worked, what did not, and where to fix it before the next stage. Professional speakers have written and rewritten these prompts for years, refining questions that dig into sentiment, themes, and audience reaction. Most of that work still stops at the transcript. It reads the words a speaker said and the words an audience wrote back, but never the way either one was actually delivered.

Why prompt libraries break down

For most speakers, a prompt library lives in a shared doc, rewritten before every big talk and forgotten between them. The prompts pull from a transcript, so they can answer what was said but not how. A keynote that lands can read identically on paper to one that falls flat: same words, same slides, same structure. The pacing that kept a room leaning in, the pause before the punchline, the flat stretch where energy dropped, none of it survives in text alone, so the prompt built to catch it never gets written.

Reading the delivery, not just the words

Speak AI runs the same prompt library against the recording itself, not just its transcript. Every talk, rehearsal, or client call is transcribed in your language, with 100+ supported, and the audio is analyzed alongside it: tone, pacing, energy, and where delivery shifts against the room’s reaction. A prompt like “where does energy drop in this talk” returns a timestamp and a pace reading, not a guess. The same prompts run across a folder of rehearsals or a season of client pitches, so a coach or speaker sees the pattern across ten talks instead of rereading ten transcripts, and dashboards you can customize and white-label chart delivery trends over time.

Prompts speakers run against their own recordings

• “Identify where pacing and energy drop across this rehearsal, and flag the timestamp.”
• “Summarize the recurring themes and sentiment in this month’s audience feedback.”
• “Pull the strongest quotes and follow-up questions from this interview.”
• “Synthesize the main findings and patterns from this round of qualitative research.”
• “Condense this focus group into the points of agreement and disagreement.”
• “Outline the narrative arc of this talk and how the room responded at each turn.”
• “Score this talk against our own delivery rubric, not a generic template.”

From a folder of talks to a coaching program

The result is a prompt library that gets sharper as a speaker or team’s catalog of talks grows. A single keynote rehearsal turns into a body of talks scored on the same criteria, with trend lines instead of one reaction from one live audience. One legal intelligence firm ran the same principle across 5,100+ hours of carrier calls and saved $700K in review time by applying one scoring engine instead of one-off reviews, and the same math holds whether the recordings are carrier calls or conference keynotes. Every prompt and every score also becomes queryable data: ask across a season of talks from Claude, ChatGPT, or Cursor through the MCP server, and the same engine that runs these prompts also powers structured call scoring and feeds coaching programs built on delivery data instead of a gut feeling.