You can tell AI-generated content in two sentences: it uses abstract nouns where a person would use specific ones, and it builds to a conclusion it telegraphed in the first line. The tell isn’t the ideas — it’s the language surface. Phrases like “in today’s rapidly evolving landscape” and “it’s worth noting that” have been reproduced so many times in AI output that they’ve become legible fingerprints, and readers — human and algorithmic — notice.
Why Models Default to Slop
Language models don’t “want” to write generically. They predict the most statistically probable next token given the preceding context. The problem is that most of the internet — the training data — is filled with average writing: SEO content, press releases, blog posts optimized for keyword density over clarity. When you give a model a vague instruction like “write a blog post about AI in marketing,” it produces the most average version of that piece, because average is what it’s been trained to predict.
“Slop” is the collective term for these high-probability, low-information outputs. It includes filler phrases, hedge-everything qualifications, circular reasoning, and abstract language standing in for specific claims. The phrases feel like writing because they have correct grammar and complete sentences. But they communicate almost nothing.
The fix isn’t to avoid AI entirely — it’s to write prompts that make the generic path harder to take. Specificity is the mechanism. When you give the model a specific persona, specific constraints, and specific things to avoid, you narrow the probability distribution toward better outputs. The seven phrases below are the most common defaults to block.
The 7 Phrases to Remove from Every Output
1. “In today’s [fast-paced / rapidly evolving / digital] landscape”
This phrase appears in roughly one-third of AI marketing and business content. It conveys no information — every era is fast-paced to someone, and “landscape” is a spatial metaphor applied to a non-spatial thing. More importantly, it tells the reader: this content was not written specifically for them.
The fix: Start with a specific observation or stat. Instead of “In today’s fast-paced marketing landscape, AI is transforming how teams create content,” try: “Marketing teams using AI for content drafting report cutting first-draft time by 60-80%, according to a 2024 Content Marketing Institute survey.” The specific version makes a claim that can be agreed with, disagreed with, or built on.
2. “It’s worth noting that…”
This phrase contributes zero semantic content. It’s a verbal filler equivalent to “um” — a way of occupying space while the model decides what to actually say. It also implies the reader needs to be told what’s worth noting, which is condescending.
The fix: Delete it and start the sentence with the actual information. The sentence after “it’s worth noting that” is almost always fine on its own.
3. “Leverage” (used as a verb)
“Leverage your existing customer base.” “Leverage AI capabilities.” This word has become meaningless in business writing from overuse. It’s also almost always replaceable by a more specific and honest verb: use, apply, draw on, build on, deploy.
The fix: Replace with the most precise verb for what’s actually happening. “Leverage” means different things depending on context — making it more specific forces you to think about what you actually mean.
4. “Delve into”
This is one of the most statistically consistent AI slop phrases, appearing far more in AI-generated text than in human writing. It’s a formality signal — a phrase that sounds like “serious writing” — and models learned to use it in contexts that call for depth. But it’s just filler for “examine,” “explore,” or simply saying what the content covers.
The fix: “This section examines…” or “The next section covers…” — or restructure to not need a transitional phrase at all.
5. “Game-changing” / “Revolutionary” / “Transformative”
Superlatives applied to mundane incremental improvements are the hallmark of product marketing written by committee, and AI has been trained on enormous quantities of product marketing. Every software update becomes “game-changing.” Every workflow tweak becomes “revolutionary.”
The fix: Describe the actual magnitude of change. “Saves 2 hours per week” is more credible and more useful than “game-changing productivity gains.” If you can’t quantify it, describe the before/after specifically.
6. “Unlock” (used as a metaphor)
“Unlock your potential.” “Unlock new revenue streams.” “Unlock the power of AI.” This metaphor was overused in human marketing writing before AI, and AI has amplified it further. The word implies capability is behind a door the reader currently can’t access — which is a weak CTA that creates dependency rather than confidence.
The fix: Be direct about what the reader can do or what they’ll gain. “Start generating ROI estimates in 30 seconds” is cleaner than “Unlock your AI ROI potential.”
7. “Robust” (applied to anything)
“Robust solution.” “Robust framework.” “Robust AI capabilities.” Like “leverage,” this word has been used so broadly it carries no information. Every product claims to be robust; the word has become noise.
The fix: Describe the specific property that “robust” is gesturing toward. “Handles datasets up to 100 million rows without performance degradation” is what “robust data processing” actually means in a specific product context.
The Prompts That Fix Slop Systematically
Editing out slop after the fact is slow. The better approach is writing prompts that make slop less likely in the first place.
The avoid list technique: At the end of every content prompt, add an explicit list of banned phrases:
Avoid the following phrases: "in today's landscape," "it's worth noting," "leverage" as a verb, "delve into," "game-changing," "unlock," "robust," "transformative." If you catch yourself about to use any of these, replace them with specific, concrete language.This technique works because the model has to actively avoid the banned terms, which redirects its probability mass toward more specific alternatives. In practice, NMM students who use this technique report that first drafts require significantly fewer edits to reach publication quality.
The specificity constraint: Add this to any content prompt:
Every claim must include a specific number, named tool, company, or example. Do not make abstract generalizations.Try it free
Jasper
Generate on-brand marketing copy, blog posts, and ad creative at scale.
This constraint forces the model away from generic phrasing because generic phrasing is usually how abstract claims are expressed.
The peer-review perspective: Instead of “write a blog post,” try:
Write this as if explaining to a peer who is as knowledgeable as you. Do not define basic terms. Do not use filler phrases to signal expertise. Demonstrate expertise through specificity and concision.Peer-to-peer framing suppresses the condescending patterns that appear when models write “for a general audience.”
Applying This in Your Workflow
The most efficient way to implement these fixes is at the prompt-building stage, not the editing stage. When you structure a content prompt with explicit Role, Task, Context, and Format fields — and include your avoid list in the Format field — you’re much more likely to get a usable first draft.
The free AI Prompt Generator lets you encode your avoid list, tone constraints, and specificity requirements in the Format field once and reuse that structure across any content task. This is faster than adding the same editing instructions at the end of every prompt manually, and it produces more consistent results when multiple team members are generating content.
For content pipelines where you’re producing articles, landing pages, or email sequences at volume, a standardized prompt template that includes your anti-slop constraints is the single highest-leverage improvement to output quality. The few-shot approach — providing 1-2 examples of the quality you want — reinforces the avoid list further. More on that in the few-shot prompting examples guide.
The Underlying Principle
The root cause of AI slop is vague instructions producing average outputs. Specificity in your prompts — specific persona, specific constraints, specific avoidances — narrows the model’s probability distribution toward outputs that communicate actual information. The seven phrases above are just the most common symptoms of a vague prompt.
This is also why chain-of-thought prompting can inadvertently produce more slop on creative tasks: when you ask the model to reason out loud before writing, the reasoning trace often contains the abstract filler language that “sounds like good writing” — and that language bleeds into the actual output. For creative and content tasks, chain-of-thought prompting is best turned off or tightly constrained.
Frequently asked questions
Does avoiding these phrases guarantee human-sounding writing? No. Removing slop phrases is necessary but not sufficient. Human-sounding writing also requires specific examples, natural sentence rhythm variation, and genuine perspective. But removing slop is the fastest and most measurable improvement because it eliminates the most obvious signals of generic output.
Are there AI detectors that specifically flag these phrases? Yes — tools like Originality.ai and GPTZero flag statistical patterns that correlate with AI generation, which includes high-frequency phrases like “delve into” and “it’s worth noting.” Beyond detection, these phrases also reduce engagement in human readers, which is a more practical reason to remove them.
Does Claude produce less slop than ChatGPT? In direct comparisons on content tasks, Claude tends to produce slightly less filler language in its defaults — but neither model is slop-free without explicit constraints. The difference between models is much smaller than the difference between vague and specific prompts. Prompt quality matters more than model choice for this particular problem.
What about AI slop in code comments and documentation? Code documentation has its own slop patterns: “This function handles…” (obvious from the code), “Note that…” (filler), and over-qualified statements (“This may potentially…”). The same specificity principle applies — describe what the code does and why the decision was made, not what it “handles” abstractly.
Should I disclose when content is AI-assisted? This depends on your platform, audience, and context. For marketing copy, AI-assistance disclosure is not yet a universal standard. For journalism and editorial content, disclosure norms are actively evolving. The practical question is whether the content is accurate and useful — AI slop that misleads is a bigger problem than AI slop that discloses. Fix the quality first.