AI COPYWRITING: HOW TO WRITE FOR LLMS WITHOUT SOUNDING LIKE AI

Many brands see AI as a quick fix for scaling copy, but with audiences quick to spot inauthentic writing, the challenge is keeping content human and trustworthy.

Copy works hard to connect with subscribers. Every push notification, recommendation tile, or piece of service messaging shapes how users feel about a platform. If that touchpoint comes across as robotic or impersonal, it breaks trust. And in a market where content fatigue is real and loyalty is key, those small breaks quickly add up to churn.

A Simon-Kucher study found that 63% of users planning to cancel a subscription cite at least one content or UX-related reason. And according to McKinsey, 71% of consumers now expect personalized interactions, and 76% get frustrated when they don’t see them.

For streaming brands, authenticity, cultural fluency, tone, and rhythm aren’t optional. They’re the foundation of UX, and they directly influence whether a subscriber feels understood or unseen. Personalized, human-sounding content strengthens loyalty. Robotic AI copy undermines it.

At the same time, discoverability matters. Content must still rank in LLM-driven search, where scannable, helpful, and informational copy performs best. The challenge is balancing machine-readability with human resonance.

The solution isn’t “AI versus human.” It’s strategic integration: knowing when to lean on AI’s strengths in speed, grammar, and consistency, and when to inject human insight, nuance, and authenticity. That’s how platforms can scale content without sacrificing trust, UX, or personalization.

HOW TO WRITE FOR LLMS – AND WHY IT’S IMPORTANT

LLMs and AI Overviews are on the rise. Today, over 13% of all Google queries trigger an AI-generated overview, making them a critical new layer of discoverability. AI search is predicted to take over traditional search engines by 2028, so ranking here isn’t optional – it’s the new front door to global audiences.

But showing up in these results is a tricky balance. LLMs favour content that is structured, scannable, factually accurate, and helpful. At the same time, they penalize vagueness, fluff, or errors.

The challenge: around 70% of consumers say they “somewhat trust” information provided by generative AI. That means the perception of trust is still fragile. And trust is key for subscriber loyalty and retention. If your brand’s content surfaces in an overview but reads as robotic, generic, or disconnected, it risks reinforcing skepticism rather than building confidence.

In practice, this means that the clearer the phrasing, the better the content performs in natural-language prompts like “What’s a good show to watch if I liked Beef?” or “Movies starring Ryan Gosling.” These prompts can filter into blog material, or even on-platform copy.

AI also favors content that’s easy to digest. That means bulleted or numbered lists where they add clarity, short and direct sentences, and language that avoids vagueness or clichés. The more structured and scannable the copy, the more likely it is to perform well in LLM-driven overviews and search.

But structure alone isn’t enough. Local nuance matters just as much. Google AI Overviews are now live in 200+ countries and territories, across more than 40 languages, and cultural context fundamentally shapes how content is received. A Brazilian drama built on telenovela traditions is a completely different viewing experience than a K-drama from South Korea. Both are technically “dramas,” but with unique storytelling rhythms, tones, and audience expectations.

Humanized copy that reflects specific local insights signals respect, fluency, and relevance. That authenticity not only helps platforms resonate with subscribers; it also strengthens the trust signals that LLMs look for when surfacing authoritative, high-quality answers.

This is why streaming platforms must optimize for both machine readability and human resonance. The copy needs to be AI-friendly enough to rank, yet human enough to engage, reassure, and feel authentic. Striking this balance directly shapes whether audiences trust your content once they see it.

RECOGNIZING THE SIGNS OF AI COPY (AND FIXING THEM)

We asked ChatGPT-5 to generate three synopses for the 2023 Barbie film:

Version 1: “Barbie leaves her perfect pink world for the real one, questioning identity, patriarchy, and purpose in a clever, colorful, and heartfelt journey.”
Version 2: “When Barbie starts malfunctioning, she visits the real world to find answers—only to spark chaos, self-discovery, and a Ken-fueled revolution.”
Version 3: “In Barbie Land, everything’s perfect—until Barbie questions existence. A wild ride through feminism, fun, and finding meaning in a plastic world.”

AI, like humans, has its own DNA. Structural fingerprints. The classic em dash. The noticeable starting arguments. The randomized bulleted list in a blog. The double emoji. One of the clearest tells of AI-generated copy is repetition, not just of ideas, but of structure and phrasing. Sentences often echo each other.

ChatGPT included a wide range of AI “tells” in these short Barbie synopses:

Overused punctuation or formatting: Em dashes, too many repeated lists and sentence structures. Two of the three synopses follow a “clause + en dash + clause” structure. One version repeated the word “Barbie” multiple times.
Looped or spoiler-heavy phrasing: AI-driven synopses often overemphasize the last 10–15 minutes of a show, leading to complaints on Reddit about spoilers and inaccurate descriptions. This is a clear case where human oversight is essential. The “Ken-fueled revolution” in the second synopsis hints at these spoilery tendencies.
Tone that feels artificial: AI tends toward safe, overly formal, or corporate “business speak”. These don’t resonate with viewers and break immersion. Here, the third version’s claim that Barbie “questions existence” feels overblown and off-tone.
Rigid structural templates: All three versions use a list of three to describe the broader themes of the film. This predictable pattern (and others like it) lack narrative flow, surprise, and spontaneity. Humans naturally vary rhythm, ask rhetorical questions, or insert quotes to keep content engaging.

While all three of the synopses are readable, the repetition and rigid structures make them feel formulaic. Human edits, adjusting rhythm, trimming redundancy, injecting personality, and highlighting local nuance, would turn these synopses into engaging, authentic copy that connects with audiences.

FINAL THOUGHT

Here’s the bottom line: using AI in the writing process isn’t the problem. Sounding like AI is.

The challenge is to harness AI’s strengths – speed, efficiency, scale – without losing the voice, cultural nuance, and authenticity that keep audiences engaged. AI can brainstorm, outline, and refine. But only humans can inject the tone, rhythm, and local insight that make content feel personal instead of robotic. That human layer is what prevents churn and builds trust.

Discoverability is also essential. Ranking in LLM search is now as important as ranking in traditional search. The way to succeed isn’t to chase algorithms but to share the kind of content algorithms are trained to show: structured, factually accurate, contextually rich, and unmistakably human.

For streaming brands, this means balancing automation with authenticity: using AI to increase reach and efficiency, while ensuring content resonates locally, strengthens trust, and keeps subscribers engaged.