You can tell AI wrote it in two sentences. The hedging. The balance. The em dashes. The corporate warmth. "Certainly! I'd be happy to help with that." It's not that AI writing is bad — it's that it's predictable. And predictable is the opposite of interesting.
This post gives you the prompting framework to fix it.
Why AI writing has a signature style
Models are trained on human feedback from raters who reward "helpful, harmless, honest." That tends to mean: balanced, cautious, qualified, warm. The training shapes the output. The model isn't trying to sound corporate. It's optimizing for what got positive signals during training.
Which means to get human-sounding output, you have to actively instruct against those defaults. You're not fighting bad writing. You're fighting well-rewarded writing that happens to be annoying.
The 7 patterns that make AI writing instantly recognizable
Pattern 1: The hedge
What it looks like: "It's worth noting that this approach has some limitations." "It's important to understand that..." "One should consider..."
Why the model does it: It's trained to be accurate, so it qualifies everything to avoid being wrong. A hedged claim is harder to dispute than a direct one.
The fix: Replace with the actual claim or cut the qualifier entirely. "This approach has one key limitation: it breaks under load." No preamble. Just the information.
Pattern 2: The balance reflex
What it looks like: "On one hand, X has significant advantages. On the other hand, it does come with certain drawbacks. It's important to weigh both sides carefully."
Why the model does it: It's trained to be fair. So it presents both sides even when you asked for a recommendation. You asked "what should I use?" and you got a policy briefing.
The fix: Prompt for a stance, not a survey. "Give me your actual recommendation. Don't list both sides — tell me what you'd do." If you want nuance, ask for it specifically. But "here are the tradeoffs" shouldn't be the default response to a direct question.
Pattern 3: Transition filler
What it looks like: "Furthermore, it is important to note that... Additionally, one should consider... Moreover, research suggests... In conclusion..."
Why the model does it: It learned that "well-structured" essays use connective tissue. So it adds connective tissue everywhere, even when the connections are obvious.
The fix: Delete every "furthermore," "additionally," "moreover," "in addition," and "in conclusion" from the output. All of them. If ideas are genuinely connected, the content itself shows that. You don't need a word that says "here comes another point."
Pattern 4: Listicle-ifying everything
What it looks like: You ask for an explanation of a nuanced idea. You get 5 bullet points with bold headers, each one a fragment.
Why the model does it: Lists signal structure. Structured output got rewarded. So the model learned to reach for bullets whenever the content has more than two parts.
The fix: Be explicit. "Write in prose, not bullet points." Or: "Only use a list if there are genuinely enumerable items — not just ideas that could be separated with commas." Most "lists" in AI output are paragraphs that got flattened.
Pattern 5: Starting with the definition
What it looks like: "Prompt engineering is the practice of crafting inputs to large language models in order to elicit desired outputs. In recent years, this field has gained significant attention..."
Why the model does it: It's trained to be informative. So it starts by establishing shared understanding — exactly like a Wikipedia article, which is probably what it learned from.
The fix: "Do not start by defining the topic. Assume the reader knows what it is. Get to the interesting part immediately." The reader Googled your blog post because they already know what the topic is. They want to know what you think about it.
Pattern 6: Corporate warmth
What it looks like: "Certainly!" "Great question!" "I'd be happy to help with that!" "Absolutely!" "Of course!"
Why the model does it: It's trained to be pleasant. Customer service language is pleasant. So it mimics customer service language.
The fix: Explicitly ban it. Put this in your prompt: "Never start with 'Certainly', 'Great question', 'Absolutely', 'Of course', or 'I'd be happy to.'" List them out. The model won't infer that you want less corporate warmth — you have to say it.
Pattern 7: Em dash overuse
What it looks like: "The model — which was trained on human feedback — tends to — as you might expect — produce hedged output — particularly in longer form content."
Why the model does it: Em dashes signal sophistication. The model overuses them as a sophistication shorthand. One em dash per sentence becomes three.
The fix: "Use em dashes at most once per paragraph. Rewrite the rest as regular sentences." One em dash is a stylistic choice. Four em dashes is a tic.
The human-writing prompt framework
Here's the wrapper prompt that addresses all seven patterns. Copy it, fill in the brackets, use it:
Write [CONTENT TYPE] as [PERSONA — e.g., "a developer who has built this system"] for [AUDIENCE].
Voice and tone:
- Direct and confident — state opinions, don't survey both sides
- Conversational — use contractions (don't, you'll, it's, that's)
- Varied sentence length — mix short punchy sentences with longer explanatory ones
- First person where natural
Avoid:
- Hedging language: "it's worth noting", "it's important to", "one should consider"
- Transition fillers: "furthermore", "additionally", "moreover", "in conclusion"
- Corporate warmth: "certainly", "great question", "I'd be happy to", "absolutely"
- Starting with a definition of the topic
- Em dashes more than once per section
- Excessive bullet points — use prose for ideas, lists only for genuinely enumerable items
Do not write an introduction that summarizes what you're about to say. Get to the substance immediately.
Length: [TARGET LENGTH]
The persona line matters more than it looks. "Write as a developer who built this" produces different output than "write a blog post about this." The model has to inhabit a perspective, not just describe a topic.
Before/after examples
The patterns are easier to see in contrast. Here's what they look like in the wild — and what the fixed version sounds like.
Blog intro
Before:
"In today's rapidly evolving digital landscape, artificial intelligence has become an increasingly important tool for content creators. It's worth noting that while AI writing assistants offer numerous advantages, there are also certain considerations to keep in mind. In this comprehensive guide, we'll explore how to get the most out of AI writing tools."
After:
"Every AI-written blog post sounds the same. The hedging. The 'furthermore.' The 'it's worth noting.' You've read a hundred of them. Here's how to stop producing them."
The "before" has five of the seven patterns in three sentences. The "after" has none. It's also shorter and more likely to get read.
Email subject lines
Before:
"Certainly! Here are some subject line options for your email campaign. These suggestions aim to balance engagement with clarity: 1. 'Exciting Updates From Our Team' 2. 'We'd Love to Share Some News With You'"
After:
"Re: your Q3 numbers" / "You're leaving money on the table" / "We ran the numbers. You should see this."
The "before" is meta-commentary on subject lines. The "after" is subject lines. When you prompt for outputs, prompt for the output directly — not a presentation of options wrapped in pleasantries.
LinkedIn post
Before:
"I'm thrilled to share that I've been exploring the fascinating world of AI tools for productivity. It's truly remarkable how these technologies are transforming the way we work. I believe that staying current with these developments is absolutely essential for professionals in today's market."
After:
"I spent 3 hours trying to get AI to write like a human. Here's what I learned: the problem isn't the model. It's the prompt."
The "before" is every LinkedIn post. The "after" is a hook. The difference is that the "after" says one specific thing instead of gesturing at a topic.
How to fingerprint your own writing style
Describing your voice abstractly doesn't work. "Write in a conversational but professional tone" produces the same output every time. The model doesn't know what your conversational sounds like.
What works: give it samples. Take 3 to 5 pieces of writing you actually like — yours, someone you admire, a newsletter you read — and use this prompt:
"Analyze the voice, sentence structure, and stylistic patterns in these samples. Note the average sentence length, how often the writer uses fragments, their relationship with qualifiers, and any recurring structural choices. Then write [X] matching that style as closely as possible."
The model is good at pattern-matching. Give it a pattern to match.
When you don't need any of this
Not all content needs to sound human. Technical documentation, code comments, meeting summaries, structured reports, first drafts you're going to rewrite anyway — for those, default AI output is fine. Clear and organized beats stylistically interesting when someone is trying to find a specific piece of information.
The "sound human" goal is for customer-facing content where voice actually matters: blog posts, social updates, marketing emails, landing page copy. Places where a reader's first impression of you is the writing itself.
For everything else, let the model be the model. Save the prompting effort for when it counts.
Start seeing the patterns
These patterns compound. Once you notice the hedge, you see it everywhere. Once you know the model's balance reflex, you catch it before it gets into your copy.
The prompts in this post aren't magic. They're instructions that override the model's trained defaults. State the opinion instead of both sides. Cut the qualifiers. Ban the corporate warmth. Write the actual thing, not a preamble to the thing.
Check out the prompt library for copy-paste writing prompts that already have these anti-patterns baked in — so you don't have to add them manually every time.
