4 Tips for Distinguishing AI-Generated Text from Human-Written Text
We asked CEOs and Founders for their best tips on how to differentiate between AI-generated text and human-written text. From looking for a lack of nuance to recognizing procedural generation, here are four insights from those who work closely with AI.
- Look for a Lack of Nuance
- Identify Inconsistent Tone
- Spot Overly Consistent Style
- Recognize Procedural Generation
Look for a Lack of Nuance
A tip for distinguishing AI-generated text from human-written text is to look for a lack of nuance and personal insight in the writing. AI-generated content tends to be more formulaic, offering generic or repetitive explanations without deeper context or unique personal perspectives. Human-written text, on the other hand, often includes specific anecdotes, opinions, or emotions that stem from real-life experiences.
This tip is helpful because it taps into the human tendency to express individuality and subjective reasoning, which AI, despite its advanced algorithms, often struggles to replicate convincingly. By focusing on the presence of personal experience or a distinct voice, it's easier to spot the difference between a machine-generated response and a human one.
Identify Inconsistent Tone
One tip I've found useful for distinguishing AI-generated text from human-written text is to look for subtle inconsistencies in tone or overly structured responses. AI often excels at producing well-organized content, but it can sometimes lack the natural variation in tone, flow, or emotion that a human would naturally include. For example, AI will write in a formal or consistent tone even when switching between topics that need more conversational or context-change.
This tip is helpful because humans tend to write with inconsistent tones, and their responses often reflect emotions, which is not found in AI texts.
Spot Overly Consistent Style
One tip I can offer for distinguishing AI-generated text from human-written content is to pay close attention to consistency in tone and style throughout the piece. AI-generated text, even though advanced, often exhibits a robotic or overly-consistent writing style. Human writers, in contrast, tend to vary their sentence structure, inject personality, and show a natural ebb and flow of emotion. Recognizing these subtleties can help business leaders detect AI involvement and decide how best to integrate or refine AI-generated content for their brand.
In my experience with the Christian Companion App, we heavily use AI tools like ChatGPT for marketing, from generating blog posts to video scripts. I noticed early on that while the AI could produce impressive text quickly, there was often a 'too perfect' feel to it, especially in longer pieces. For example, AI might maintain a formal tone even in sections where a human would naturally switch to something more casual or conversational. We trained our team to spot this by looking for overly-polished, consistently-neutral phrasing that lacks the human touch of spontaneity. This strategy has helped us refine AI content to feel more authentic and engaging.
To address this issue, businesses should train their team to compare AI output with natural human writing. The key is blending AI efficiency with human nuance. One simple approach is to edit AI text for variation in tone and style, adding more diverse sentence structures or casual phrases that feel more relatable to the reader. This method allows you to use AI's speed and precision while ensuring your content retains the authenticity of human writing.
By combining AI efficiency with the human touch, we've found a balance that increases productivity without sacrificing quality. Our employees spot these differences early and can tweak content to make it sound more human, giving us an edge in both speed and resonance. This attention to detail not only improves engagement but ensures our brand feels personal, even in an increasingly AI-driven world.
Recognize Procedural Generation
I often hear people joke that "it sounds like an AI wrote this" when they encounter low-quality text—specifically text that uses jarring syntax, has uncommon errors, and maybe includes concepts associated with the given topic but doesn’t relate those concepts to each other in a "human" way. This is a pretty good description of what AI-generated text used to sound like two or even three years ago, but it’s deeply outdated now. Today, LLMs write with basically no errors, use "normal" syntax, and speak coherently and often knowledgeably about most topics.
The way to recognize AI-generated text vs. human-written text is mainly a process of elimination, along with getting a feel for a certain vibe. One telling characteristic is slight wordiness—nothing crazy, just the sneaking feeling that perhaps a word count is trying to be reached and the ratio of words used vs. necessary words is a bit skewed.
Other tells: AI-generated text doesn’t tend to repeat words. If it has to talk about the same thing at length, it’ll find ways to vary the wording it uses to refer to that thing. And while LLMs are good at varying their wording, they generally stick to a "mainstream" pool of words that feel eloquent but still well-known. In contrast, people often have their own favorite uncommon words that stick with them and crop up in their thinking and writing, so if you’re seeing "eclat" or "nadir" in a piece of text, it’s probably not straight from an AI.
AI writing also tends to be fairly linear; list-like structuring is common, even in prose or paragraph form, often with a full-circle return to the overall concept at the end of the text. Because a human writing a piece of text usually possesses a full mental framework of what they’re writing about before they even begin to write, they may choose a nonlinear "starting point" and "ending point" for their text, and explore the space between those points nonlinearly as well. You get the sense that the author is looking at a complete picture and making strategic choices about how and when to share parts of that complete picture with you. Because LLMs generate text in a procedural way, it can be difficult for them to mimic that feeling. The moments when they do achieve complexity and nuance tend to be later in the text, as they stack concepts on top of each other, brick by brick.