How to Train AI for Bulletproof Brand Voice: Top Tips and Tricks

In late 2023, Sports Illustrated became ensnared in the editorial version of a doping scandal — the outlet was caught publishing dozens of AI-generated articles under fake bylines. The fallout was swift. Within days, the editor-in-chief was fired and the brand’s credibility took a beating.

Though the SI snafu occurred in the early, Wild West days of ChatGPT’s mainstream adoption, its lessons linger two years later. The sloppy AI articles eroded reader trust — a precious and tenuous commodity in today’s world of fake news and algorithm-fueled outrage.

While marketers have different stakes than media outlets, they’re playing with the same volatile mix of automation and audience expectation. As every B2B marketer who’s had to scrub the phrase “rapidly evolving tech landscape” from an AI-generated blog post knows, chatbots have a tendency to produce generic platitudes or even blatant misinformation.

Don’t get me wrong: AI has plenty of upside. It can help you scale your content like never before. But only if you teach it to sound unmistakably like you — and keep a watchful eye on its work.

Here’s how to avoid becoming the next cautionary tale.

Put up guardrails before you unleash the bots

Marketers are getting more hands-on with the fine-tuning and orchestration behind generative AI engines. You might be building a custom GPT to answer customer questions in your brand’s tone, feeding a writing assistant AI your top-performing articles for inspiration, or integrating AI into your CMS or email workflows to auto-generate first drafts.

All these cases involve understanding the basics of training AI on brand-aligned inputs and clear intent signals. Train a chatbot well, and it can produce remarkable work. Leave it to guesswork and vague direction, and it will confidently wing it with results that may sound professional but miss the mark in any number of ways.

Savvy content teams use a three-layered safety net that any team can implement quickly, regardless of technical expertise:

1. Start with reusable prompts. These are essentially scripts that the AI must follow every time it writes for you. Specify exactly who it’s speaking to, which tone to use, and which words or topics are off-limits.

2. Add a built-in cheat sheet. Retrieval-Augmented Generation (RAG) sounds intimidating, but the concept is simple: Instead of relying only on what a model remembers, RAG lets AI pull relevant facts from a trusted source — your database of approved quotes, product specs, or brand guidelines — as it writes. This gives the AI a live reference doc to consult so it stays grounded in accurate info.

3. Layer in quality control. Run every draft through an automated style checker to flag banned words and tone inconsistencies. Then, have a human editor do the final sweep for nuance and legal compliance.

Start cautious with heavy human oversight, then gradually automate more as your guardrails prove reliable. The beauty of this system is that it scales with your confidence.

Feed AI great examples, not a data dump

Your first instinct might be to feed an AI model every piece of content you’ve ever published — but resist that urge. Just as with onboarding a new writer, when it comes to AI-assisted content creation, quality trumps quantity.

In other words, a few dozen pieces that perfectly capture your voice will teach an AI system better than thousands of mediocre examples mixed with outdated content that no longer reflects your brand.

Here’s a three-step playbook for this process:

1. Start building a “gold standard” dataset with content that already works. This might involve flagship blog posts that have performed well in the past, genuine thought leadership, landing pages with strong conversion rates, or customer support emails that have received positive responses.

2. Give it rich context. Tag each piece with metadata about audience, funnel stage, geographic region, and any compliance requirements. This teaches the AI when to be playful (like for a social media post) and when to stay clinical (for a technical white paper).

3. Be intentional with what you leave out. Not every high-performing asset belongs in your training set. If a piece doesn’t reflect how you want the AI to write going forward, don’t include it — no matter how well it performed at the time.

Test, tune, and toss what doesn’t work

Once your guardrails are solid and content examples carefully curated, you can start adjusting the AI’s output to match your voice more precisely. Think of this phase like onboarding a talented new employee who understands the basics but needs to learn your company’s specific way of doing things.

Start by cleaning up your training materials. Delete boilerplate text or legal footers that might confuse the model. AI systems learn patterns quickly, so you want them picking up your unique voice — not generic jargon that appears in thousands of other companies’ content.

Here are a few best practices to consider at this stage:

1. Choose your level of intervention carefully. For most brands, lightweight adjustments using Low-Rank Adaptation (LoRA) work well — they’re fast, affordable, and often effective for subtle voice tweaks. Full model retraining, on the other hand, is expensive and time-consuming. The latter should be reserved for companies with truly distinctive voices (and big budgets).

2. Test systematically. Split your examples into training, validation, and testing groups using a 70/20/10 ratio. Have human editors rate the AI’s output on tone and accuracy without knowing which pieces are AI-generated versus human-written. This blind testing reveals whether your training actually improved the voice match or just taught the AI to mimic surface-level patterns.

3. Finally, make sure the math works. If the cost of GPU time and platform fees exceeds the editing hours you save within six months, pause and reassess your approach. AI should make your team more efficient, not drain your budget on computing costs.

People power your AI’s potential

Even the smartest content marketers run into predictable AI stumbles. “Tone drift” happens when an AI’s voice gradually veers off-brand over time. “Grand sentence syndrome” is another frequent offender — you know, those overly complex, academic-sounding phrasings that no human would ever utter in a casual conversation. Then there are punctuation quirks (hello, endless em dashes and gratuitous gerunds) and hallucinations, when AI confidently fabricates facts out of thin air.

People are the secret sauce that can turn AI from a liability into a differentiator. Today’s content teams need solid talent to fine-tune the tech and enforce editorial standards, including:

• Prompt architects who know how to steer tone and structure through careful A/B testing
• Model specialists who can evaluate which tools and settings deliver the best results for each content type
• Journalistically minded editors with strong fact-checking chops to catch red flags before a piece publishes

AI can amplify everything that makes your brand voice memorable, or it can flatten that personality into forgettable corporate-speak. The deciding factor isn’t the size of your dataset or sophistication of your model — it’s the clarity of your guidelines and the expertise of your editors.

Want AI to nail your brand voice without the headaches? Contently’s AI Studio takes care of the setup, fine-tuning, and editorial oversight — so you get better content, faster, and with less risk. Chat with us today to scale faster and sound better doing it.

Frequently Asked Questions (FAQs)

What’s the biggest risk of using AI in content marketing?

The short answer: sounding generic or getting facts wrong. Without strong guardrails, AI tends to default to safe but stale phrasing — or worse, confidently fabricates misinformation (a.k.a. hallucinations). That’s why the most effective teams pair AI tools with human editors, prompt testing, and fact-checking systems that keep brand voice sharp and content credible.

How much content do I need to train an AI on my brand voice?

Less than you think — as long as it’s the right content. A few dozen examples that clearly reflect your tone, structure, and audience fit are far more valuable than a massive archive of outdated or inconsistent pieces. Focus on quality over quantity, and tag each piece with helpful metadata like audience, funnel stage, and channel to give the AI proper context.

How can I tell if my AI training efforts are actually working?

Treat it like a science experiment: Split your sample into training, validation, and test sets (think 70/20/10). Then, have human reviewers rate the outputs without knowing which were written by AI and which weren’t. If your team can’t consistently tell the difference — or if AI-generated drafts require fewer edits — you’re on the right track.