AI Content Factory and Brand Consistency

Activate when: user says 'use AI to mass-produce content', 'how do we make AI write like our brand?', 'the AI content got facts wrong', 'how do we keep AI content out of legal trouble?', 'build a content factory', or plans 10+ AI-assisted pieces per week (blog, social, email, product pages) across several writers or agents. Do NOT activate when: writing a single one-off piece of copy; brand positioning and target customer are not yet decided (position first); the question is how content drives growth at all (use content-led-growth). More: deciqai.com/c/ai-content-factory-brand-consistency

Overview

AI drives the marginal cost of a draft toward zero, so the bottleneck moves to consistency, accuracy and compliance. Volume without gates is brand dilution, factual error and legal exposure, multiplied by output.

The core mechanism: specify the inputs (a voice spec and a fact base) → generate → pass every piece through risk-tiered gates → feed quality measurements back into the spec. Humans stop writing first drafts and start guarding gates.

Compose with content-led-growth (why content compounds) · trust-brand-architecture (what a factual error costs in trust) · checklist (the shape of each gate) · goodharts-law (don't chase piece count) · ai-output-verification-trust-tiers (tiering any AI output by error cost).

When to Use

• Output must rise from a few pieces a month to dozens a week
• Several people or agents write, and tone and claims have started to drift
• Content carries accountable claims: prices, efficacy, comparisons, customer reviews, regulated topics

When NOT to use: a single low-risk piece · positioning not yet decided · a strictly regulated industry with a mandatory compliance workflow (this process supplements it, never replaces it).

Coaching Novices (Adaptive Front Door)

• Engine mode: user has a concrete brand and content plan → run The Process directly.
• Coach mode: user is unfamiliar → guide one step at a time.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

1. What it is: "First write down how the brand talks and which facts it may state. Then let AI produce at volume, with humans guarding the gates."
2. Check fit: "How many pieces a week are you planning? Do any include prices, efficacy, comparisons or reviews?"
3. Elicit the real case: "Send 3 pieces that sound most like your brand, and 1 that sounds least like it."

[WAIT — do not advance until user responds]

4. Run The Process: "From your samples, here is a draft voice spec — 3 attributes, banned words, good/bad pairs. Correct it."

[WAIT — do not advance until user responds]

5. Name the insight: "Consistency comes from a spec and gates, not from a better prompt."

The Process

Output: Content Factory Runbook (voice spec + fact base + gates + cadence + metrics)

1. Voice spec. 3–5 tone attributes written as is / is-not pairs, banned words, sentence length, person, and 3 good/bad example pairs. Gate: two reviewers score the same draft differently → the spec is too vague; rewrite it → stop.
2. Fact base (single source of truth). Product specs, prices, case data, customer-approved quotes — each entry with source, expiry date and owner. Gate: any number or quote in a draft that is not in the fact base → reject the draft.
3. Risk tiers. Low (opinion, tutorials) · Medium (product descriptions) · High (prices, efficacy, comparisons, reviews and testimonials, legal / health / financial topics). Gate: no high-risk piece publishes without a line-by-line human check against the fact base.
4. Generate → review gates. AI drafts → automated checks (banned words, fact-base match, links, required AI disclosure) → human spot-check (low) or full review (medium and high). Gate: any claim that cannot be traced to a source → delete it or rewrite it as a provable statement.
5. Style QA. Sample weekly and score voice fit 1–5; write low-scoring samples back into the spec as bad examples.
6. Cadence and metrics. Fix the publishing frequency; track quality (dwell, replies, conversion, corrections issued), not just count. Gate: correction rate rising for consecutive periods → cut volume and fix the spec first.
7. Stop-rule: no one will own the fact base → halt volume production and publish only low-risk opinion content until someone does.

Output template: voice spec v_ | fact-base entries / expired | risk-tier rules | gates (auto / human) | cadence | quality metrics | corrections this week | spec revision log

→ Method in Action: CNET's AI-written finance explainers (2022–2023)

Applying It Well

1. Specify with examples, not adjectives. "Professional but friendly" cannot be executed; a good/bad pair can.
2. The fact base is an asset with a shelf life. An expired figure is more dangerous than a missing one — set expiry reminders.
3. "A human edited it" is not a gate. Review without a source check is reading, not verifying.
4. Disclose visibly. Where content was AI-assisted, say so where readers will see it, per the rules that apply where you publish.
5. Never fabricate reviews, testimonials or case studies. Treat endorsements and AI disclosure under the rules of the jurisdiction you publish in.

→ Primary sources: references/sources.md

Common Rationalizations

[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.

Fake move	Reality
[D] "A good prompt is enough"	A prompt does not replace a fact base and gates; the model still fabricates.
[D] "Volume will find the hits"	Low-quality volume dilutes the brand and search trust.
[D] "Review is too slow"	Tier by risk. Low-risk pieces get spot-checks; only high-risk pieces are slow.
[D] "An editor already looked at every piece"	Looking is not checking. Without a fact-base match, errors pass a human reader.
[D] "Errors written by AI aren't ours"	The publisher is responsible for what it publishes.
→ Add [O] entries here after each real use — paste the actual failure pattern	What went wrong and why

Red Flags

• Publishing numbers with no fact base · counting only pieces published · a voice spec made of adjectives
• Reviews or case studies of unknown origin · no named owner per piece · AI disclosure hidden behind a hover

Verification

• Voice spec has good/bad examples, and two reviewers score the same draft the same way
• Every number and quote traces to a fact-base entry with source and expiry
• High-risk pieces carry a record of the line-by-line check
• Metrics include quality and corrections, not only volume

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Part of deciqAI Knowledge Skills — 253 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/c/ai-content-factory-brand-consistency · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.

Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/ai-content-factory-brand-consistency.json