Marketing-Specific Prompt Patterns — Voice Preservation, Banned-Pattern Enforcement, Tone Calibration

Source: wiki synthesis: Banned AI Patterns, Voice Profile Extraction, OmniPresence System, Module 1 — Prompts as Reusable Artifacts

The wiki has three well-developed, production-tested prompt patterns for making Claude write in someone else’s voice while suppressing generic “AI-sounding” output — but they’ve lived exclusively inside ai-video-content/ as dental-marketing production rules (OmniPresence’s script pipeline). Stripped of the dental framing, they generalize to any marketing, brand-voice, or ghost-writing prompt work. This article extracts the three patterns as general prompt-engineering technique.

Key Takeaways

• Banned-pattern enforcement is constraint stacking made checkable. A flat list of forbidden words/phrases works better than “sound natural” because Claude can verify against a list but can’t verify against a vibe. The technique scales: 5 constraints in v1, 15+ by v3, each one earned from an observed failure.
• Voice preservation works by extraction, not description. “Warm and plainspoken” is nearly useless as an instruction — it’s abstract, and Claude has to invent what it means. Pulling actual signature phrases, sentence-length patterns, and real stories from source material (a transcript, past emails, existing copy) and feeding those back as <examples> transfers voice with far higher fidelity than adjectives ever do.
• Tone calibration is a named-reviewer self-critique step, not a rule. Asking Claude to read its own draft as a specific person with known pet peeves (“read this as Mel, who hates corporate jargon and rhetorical-question openers”) catches judgment-call violations that a checklist can’t enumerate — the things that are technically compliant but still read wrong.
• These three patterns compose in a fixed order: extract the voice first (from real material), draft, then filter against the ban list, then critique in the reviewer’s voice. Running critique before extraction just produces confident-sounding genericness with nothing real to check it against.
• Session isolation is the operational failure mode nobody’s constraint list catches. Voice bleed — one client’s or brand’s voice phrases leaking into another’s output — happens at the session level, not the prompt level. The fix is procedural (one identity loaded per session, fresh session on switch), not a prompt addition.

Pattern 1: The Checkable Ban List (Constraint Stacking)

The core move, generalized from Banned AI Patterns’s 99-phrase dental-marketing list: don’t tell Claude to “avoid sounding like AI” — give it an explicit, growable list of forbidden words and structural patterns it can check its own output against.

Two tiers of ban:

• Word/phrase level — “diving into,” “unlock,” “leverage,” “game-changer,” “at the end of the day,” and similar generic-marketing-copy tells. These transfer to any domain; a WEO onboarding module already generalizes this exact list (Module 1’s constraint-stacking technique uses the identical phrase set).
• Structural-pattern level — this is the less obvious, higher-value half of the technique. “No X. No Y. Just Z.” sentence fragments, stacked short-punchy-fragment lists (“One visit. Done.”), standalone rhetorical-question openers (“The good news?”), and em-dash overuse are AI structural tells that survive a word-level ban list untouched. A generic word filter misses all of them; only naming the pattern catches it.

Why it outperforms “write naturally”: Claude can run a genuine check against an enumerated list (per Module 1’s validation-with-retry technique: “Output a <validation> block listing each banned phrase and whether you found it in your draft” — forcing the listing makes the check real instead of performative) but cannot meaningfully self-assess against an adjective like “natural.” The list compounds over time: start with 5 rules, add one every time a new failure mode is observed, and by the third production cycle you have 15+ rules earned the hard way rather than guessed upfront.

Pattern 2: Voice Extraction From Source Material

Voice Profile Extraction’s five-category framework — signature phrases, speech patterns, key stories, personality markers, recurring topics — is a general method for building a voice profile from any real transcript, email archive, or writing sample, not just a dental-practice interview:

• Signature phrases: coined terms and metaphors the source repeats more than once. Repetition is the signal that a phrase is genuinely theirs, not a one-off.
• Speech/sentence patterns: short-punchy vs. flowing, tag questions, contractions, where tone shifts from casual to serious.
• Key stories: specific, real, told-with-energy examples — never fabricated, never paraphrased into genericness.
• Personality markers: what they get animated about, what they’re proud of, what values surface unprompted.
• Recurring topics: what they return to across the source material without being asked — this usually reveals what they actually want to be known for.

The extraction feeds directly into Module 1’s multi-shot-examples technique: 2–3 real on-voice examples in <examples> tags transfer sentence rhythm and implicit “what we don’t say” far better than any prose description of tone — “examples teach voice; rules teach what not to do,” and the two belong in different sections of the same prompt. The generalization beyond dental marketing: any ghost-writing, brand-voice, or executive-communications prompt benefits from treating a real transcript or writing sample as the primary source, not a set of adjectives someone used to describe the person.

Pattern 3: Self-Critique as a Named Reviewer Persona

The highest-leverage, least-obvious pattern. Module 1’s Technique 8 (self-correction) and the OmniPresence pipeline’s quality gate converge on the same move: after drafting, have Claude critique its own output as a specific named person with known pet peeves, not as a generic “check for quality” step.

Read your draft as if you were [Reviewer], [role]. [Reviewer]'s pet peeves:
[specific, concrete list — e.g. sentences that sound like a brochure,
generic opener patterns, hedging language, clinical jargon in casual answers].
List every issue you find, then produce the corrected version.

This catches what a rules-only validation pass misses: “this sentence is technically allowed but reads off-voice.” A banned-phrase list is binary (present/absent); a named-reviewer critique makes a judgment call and shows its reasoning, which a human can then agree or disagree with. The specificity of the pet-peeves list matters — “check for quality” produces a rubber-stamp pass; “Mel hates rhetorical-question openers and anything that sounds like a brochure” produces a real critique, because it gives Claude something concrete to test against rather than something to perform compliance with.

Generalizing Beyond Dental Marketing

None of the three patterns above are dental-specific — the domain material (gumline vs. gum line, Chicago Manual of Style numeral rules, “cheat code” as Dr. Browning’s coined phrase) is the input, not the technique. The technique is:

1. Extract real voice material before writing anything (Pattern 2).
2. Draft against that extracted voice.
3. Filter the draft against an enumerated, growable ban list — words and structural patterns both (Pattern 1).
4. Critique the filtered draft as a specific named reviewer with concrete pet peeves (Pattern 3).
5. Keep the whole stack in one session per brand/client identity; never load two voice profiles in the same session.

Any team doing recurring brand-voice work — agency client scripts, executive ghost-writing, personal-brand content, customer-facing support macros — can lift this five-step stack directly. The dental-specific ban list and Chicago Manual of Style rules are one instantiation of step 3; a different brand needs its own list, built the same way (earned from observed failures, not guessed upfront).

Related

• Banned AI Patterns — the source ban list and structural-pattern catalog this article generalizes.
• Voice Profile Extraction — the source five-category extraction framework.
• OmniPresence System — the two-layer (fixed structure + variable voice) production architecture these patterns were built inside.
• Mel’s Feedback Rules — the real-world reviewer persona Pattern 3 is modeled on.
• Module 1 — Prompts as Reusable Artifacts — the general constraint-stacking, multi-shot-example, and self-correction techniques these patterns are specific applications of.
• Prompt Engineering Essentials — the foundational few-shot and self-correction techniques underlying Patterns 2 and 3.
• Anti-AI Slop Guide — the design-focused (not copy-focused) counterpart ban list.

Try It

1. Build a ban list for your own recurring writing task the way Banned AI Patterns did: start with 5 rules from memory, then add one every time you catch a new AI-sounding tell in review. Include at least one structural pattern (not just words) — “No X. No Y. Just Z.” fragments are the most common tell a word-only list misses.
2. Pull 3 real writing samples from whoever’s voice you’re trying to match and extract signature phrases, sentence-length patterns, and one real story — feed those in as <examples>, not as adjectives describing their tone.
3. Add a named-reviewer critique step to your next content prompt. Pick a real person (or persona) with known, specific pet peeves and have Claude critique its own draft as that person before producing the final version.