The Voice Profile Builder
Construct a precise, reusable model of anyone’s writing voice by analyzing the delta between their drafts and their edits — the gap between “what AI wrote” and “what I actually sound like” is the most information-dense signal for voice modeling. From the Jul 17, 2025 AIMM session on building voice profiles that actually work.
You will build a voice profile — not a vague “tone and style guide” but a precise, actionable model of how a specific person communicates. The secret: the richest signal isn’t in finished writing (which is polished and therefore generic). It’s in the EDITS — the changes someone makes to a draft reveal their voice more accurately than anything they could describe in the abstract.
The mechanism: when someone edits AI-generated text, every change is a data point. “Changed ‘utilize’ to ‘use’” reveals vocabulary preference. “Cut the entire opening paragraph” reveals pacing instincts. “Added a profanity” reveals register. “Restructured from list to narrative” reveals format preference. Aggregate enough of these deltas and you have a voice model more accurate than any style questionnaire could produce.
VOICE DATA TO ANALYZE: $ARGUMENTS
If no data was provided above, ask me to provide voice samples. The best inputs (in order of signal density):
- Draft/edit pairs: AI-generated text alongside the human’s edited version (HIGHEST signal)
- Raw unedited writing: Emails, Slack messages, social posts, first drafts — the less polished, the better
- Published writing: Blog posts, articles, LinkedIn posts — useful but lower signal because editing has already smoothed idiosyncrasies
- Verbal transcripts: Meeting recordings, podcast appearances, talks — captures spoken voice
Multiple sample types produce a richer profile. Even a single draft/edit pair is valuable.
VOICE OWNER: [WHOSE VOICE ARE WE MODELING — yourself, a client, a team member, a public figure? Say “you decide” to have me infer from the data] INTENDED USE: [WHAT THE VOICE PROFILE WILL BE USED FOR — content creation, ghostwriting, brand voice documentation, AI training, personal reference. Say “you decide” to have me infer]
If “you decide,” state the inference and proceed.
STEP 1 — DELTA ANALYSIS (if draft/edit pairs are available): For each pair, catalog every change and classify it:
- Vocabulary shifts: Words substituted, added, or removed. What do the replacements reveal about formality level, jargon comfort, and word-feel preferences?
- Structure changes: Paragraph reordering, sentence splitting/merging, section additions/deletions. What do these reveal about pacing instincts and information architecture preferences?
- Tone adjustments: Where did the voice owner add warmth, edge, humor, directness, or qualification? Where did they remove polish, hedging, or corporate-speak?
- Kill patterns: What did they consistently delete across multiple samples? (This is often the most diagnostic signal — it reveals what the voice owner finds inauthentic)
- Add patterns: What did they consistently insert? (Reveals what they feel is missing from AI-generated prose — often the most personal elements)
If draft/edit pairs aren’t available, skip to Step 2 and work from the raw samples.
STEP 2 — VOICE DIMENSIONS: From the data (deltas + samples), build the profile across these dimensions:
Rhythm & Pacing:
- Average sentence length range (short/punchy, long/layered, or mixed?)
- Paragraph density (how much ground per paragraph?)
- Transition style (explicit connectors, implicit flow, abrupt shifts?)
- Opening patterns (how do they begin pieces — question, claim, story, provocation?)
Vocabulary & Register:
- Formality spectrum (where between academic and casual?)
- Jargon relationship (embraces, avoids, or subverts industry language?)
- Signature phrases (recurring expressions, pet words, verbal tics)
- Profanity/intensity comfort level
Stance & Posture:
- Default argumentative mode (builds cases, makes assertions, asks questions, tells stories?)
- Certainty calibration (how confidently do they state claims?)
- Relationship to the reader (peer, teacher, provocateur, ally, coach?)
- What they’re willing to say that others aren’t (the edge of their voice)
What They Avoid:
- Phrases, structures, or tones that are absent from their writing
- The “anti-voice” — what would sound wrong if it showed up in their content
STEP 3 — VOICE PROFILE DOCUMENT: Synthesize Steps 1-2 into a portable, reusable voice profile. Format:
[Name]‘s Voice Profile
Summary (2-3 sentences capturing the overall feel)
Do (specific, actionable instructions for writing in this voice — not “be conversational” but “lead with the claim, qualify only if the evidence is ambiguous, use sentence fragments for emphasis in lists”)
Don’t (specific anti-patterns — not “don’t be formal” but “never open with ‘In today’s rapidly evolving landscape’ or any throat-clearing setup”)
Signature Moves (2-4 distinctive patterns that are uniquely theirs)
Sample Rewrites (take 2-3 generic AI sentences and show what they would look like rewritten in this voice)
STEP 4 — CALIBRATION TEST: Write a short piece (150-200 words) on a topic relevant to the voice owner, applying the profile. Then explicitly flag:
- Which elements of the profile you’re most confident about (backed by multiple data points)
- Which elements are speculative (inferred from limited data)
- What additional samples would most improve the profile’s accuracy
Ask the voice owner to mark what sounds right and what sounds off. Each correction sharpens the model.
STEP 5 — VERIFICATION:
- Is this profile specific enough to distinguish this person’s voice from a generic “professional” or “conversational” voice? Test: could two different people share this profile? If yes, it’s not precise enough.
- Am I modeling their actual voice, or their aspirational voice? (People sometimes edit toward who they want to sound like, not who they actually sound like. Both are useful — but they should be labeled differently.)
- Are the “Don’t” items genuinely absent from their writing, or are they just less common? Only include true anti-patterns.
Revise the profile based on calibration feedback.
Source
- 2025-07-17_Mastermind (multiple — building voice profiles from draft/edit deltas)