Software and AI engineers were among the early adopters of AI and have already gone through several phases of figuring out how to use it, what works, and what doesn't. Now the rest of the world is catching up, going through many of the same phases with some delay.
Although models are much more powerful today than they were back then (and will keep improving), some problems around AI slop remain. The solution isn't always for the models to magically become better. Some of these problems require users to develop new skills, judgment, and taste.
In this article, we'll discuss some of these problems and practical ways to address them.
What is slop?
AI slop refers to low-quality, mass-produced content generated by artificial intelligence with little human thought, creativity, or critical judgment. It often appears polished and convincing on the surface but lacks substance, originality, depth, or genuine insight.
AI slop isn't defined simply by the use of AI, but by the absence of meaningful human intention and quality control. Whether it's an article, image, video, or piece of software, the defining characteristic is that producing content becomes more important than creating something valuable.
In the AI engineering community, @swyx declared war on slop in December 2025, encouraging the community to produce high-quality content for a better internet.
The general problem
Most users approach AI with simple, lazy prompts, providing little direction about what they actually want. This saves an enormous amount of time, but it also often produces slop.
Why? Because of how these models are trained. They learn patterns from enormous amounts of existing content, and when given little direction, they tend to fall back on familiar, common patterns from their training. The result is often something statistically conventional: technically correct, perhaps even impressive at first glance, but lacking specificity, originality, and taste.
Humans are different. We bring our own experiences, preferences, knowledge, and intentions into whatever we create. Even when given the same task, different people will approach it differently.
AI can produce extraordinary results, but without sufficient direction, it often gravitates toward the generic. And while better models can reduce this tendency, they cannot always know what you consider good, what you are trying to communicate, or what makes something valuable to you.
That is where the user comes in.
Tools to avoid slop
Inspiration
Sometimes it's hard to even imagine what the desired result should look like. So before generating anything, it helps to do some research for inspiration.
Explore existing work, different styles, approaches, and techniques. Look at what others have created and try to understand what makes some examples better than others. Alternatively, ask AI to generate several different directions before committing to one.
This not only improves the quality of the final result but also helps you develop taste over time. The more you expose yourself to good work and consciously compare alternatives, the easier it becomes to recognize what you like and articulate what you want.
Eventually, you'll spend less time figuring out what to ask for and more time refining the things that matter.
Prompt engineering
One strategy to avoid slop is prompt engineering, which is a fancy term for writing good instructions.
Instead of giving AI a vague request, specify what you're trying to achieve, the intended audience, the desired style, the constraints, what should be included, and what should be avoided. The more relevant direction you provide, the less the model has to fill in the gaps with its own defaults.
Although this works well, there is a limitation: describing exactly what you have in mind with words can be surprisingly difficult. Sometimes you don't even know how to articulate what makes something good.
That's why references, examples, and visual material can be just as important as the prompt itself.
Iterations
Another strategy is to start with a simple prompt and then iterate.
Rather than attempting to describe the perfect result upfront, generate an initial version, identify what is wrong or missing, and ask the model to improve those specific aspects.
This approach works with or without elaborate prompt engineering. In fact, for many tasks, it is more practical than trying to anticipate every detail in advance.
The important part is not to accept the first result simply because it looks good enough. Treat it as a draft, evaluate it critically, and continue until it meets your expectations.
The real skill here is learning to recognize what needs to change and communicating it clearly.
Strategies for specific kinds of content
The principles above apply to almost everything AI can generate, but different kinds of content have their own challenges. Let's look at some practical examples.
Images
Imagine asking AI to "generate a picture of a house."
You'll probably get a perfectly recognizable house, but there's a good chance it will look like countless other AI-generated houses. Pleasant lighting, conventional architecture, carefully arranged surroundings, and that strangely familiar AI aesthetic.
The problem isn't that the model cannot generate interesting architecture. It's that you haven't given it any reason to choose something more specific.
Instead, start by looking for inspiration. Explore architectural styles, photography, paintings, lighting conditions, and compositions. Decide what kind of house you want, what atmosphere it should communicate, and which details matter.
For example, instead of a generic house, you might ask for a small brutalist concrete house on a rocky coastline, photographed on an overcast winter morning, with minimal vegetation and an asymmetrical composition.
You can also provide reference images rather than trying to explain everything in words. Then iterate on the result, changing the composition, lighting, materials, or individual details until it feels right.
But don't just look at the overall picture. Observe the details. Does everything look consistent? Are the shadows and reflections correct? Are there architectural elements that make no sense, like a door with two knobs on the same side, stairs leading nowhere, or windows with impossible geometry? Zoom in, inspect the image carefully, identify mistakes, and iterate on them. Sometimes fixing a few small details makes the difference between an obviously AI-generated image and a convincing one.
The goal isn't necessarily to write longer prompts. It's to make deliberate creative decisions instead of leaving them all to the model.
Videos
Video generation introduces another dimension: time.
A video might look impressive frame by frame, yet feel artificial because of its pacing, camera movements, transitions, or the way objects behave. AI often defaults to dramatic camera movements, cinematic lighting, and exaggerated action because these create immediately impressive results.
But impressive doesn't necessarily mean good.
Before generating a video, think like a director. What is happening in the scene? Where is the camera? What moves, what stays still, and how long should the shot last? What emotion should the viewer experience?
Instead of asking for "a cinematic video of a city," you could describe an eight-second static shot of a rainy street at night, with a single cyclist passing through the frame and reflections moving across the pavement.
For more complex videos, break the idea into individual shots, plan their sequence, and generate them separately. Pay attention to visual consistency, continuity, and pacing during editing.
Then watch the generated video critically, preferably more than once. Do objects suddenly change shape or disappear? Do people's movements look natural? Are the lighting, shadows, and surroundings consistent between frames? Does the camera move in a physically plausible way? Look for subtle mistakes that might be easy to miss on the first viewing. Regenerate problematic shots or adjust the instructions until the inconsistencies are resolved.
Often, restraint produces a more convincing result than spectacle.
Music
Music is particularly interesting because people can recognize something that sounds generic even when they cannot explain exactly why.
Ask AI to generate "epic background music," and you'll likely get something that follows familiar patterns: predictable chord progressions, dramatic buildups, conventional instrumentation, and an obvious emotional direction.
It might sound professionally produced, but that doesn't mean it has character.
The solution is to approach music generation with some understanding of musical structure. Think about the instruments, rhythm, tempo, dynamics, harmony, and progression of the piece. Listen to music you admire and try to understand what makes it distinctive.
For example, rather than asking for "sad cinematic music," you could request a slow instrumental piece built around sparse piano notes and low strings, with unresolved harmonies, long silences, and tension that gradually increases without reaching a dramatic climax.
Generate alternatives, listen critically, and refine individual aspects. Sometimes removing an instrument or simplifying the arrangement improves the result more than adding complexity.
Pay attention to the details when listening. Do the transitions between sections feel natural? Are there strange changes in tempo, instruments that suddenly appear or disappear, unnatural vocals, or noticeable audio artifacts? Does the composition develop in a meaningful way, or does it simply repeat familiar patterns? Identify the parts that don't work and regenerate or edit them instead of accepting the entire track as it is.
Musical taste, just like visual taste, develops through listening, comparison, and experimentation.
Copy and text
AI-generated writing is probably one of the most common forms of slop, partly because it's so easy to produce and so easy to publish.
It tends to be grammatically correct, well-structured, and polished, yet strangely empty. Generic introductions, unnecessary explanations, predictable conclusions, exaggerated enthusiasm, and phrases that sound impressive without saying very much.
The problem becomes especially apparent when people ask AI to write about a subject without contributing any original thoughts.
Before asking AI to write something, decide what you actually want to say. What is the central argument? Why is it worth reading? What observations, experiences, or examples do you have that the model wouldn't know?
For instance, instead of "write a LinkedIn post about AI and software," provide your actual argument, your reasoning, and the tone you want. You might even write a rough draft yourself and ask AI to improve its structure, clarity, and grammar without changing your voice.
Providing samples of writing you admire can also help, as can explicitly asking the model to avoid clichés, unnecessary adjectives, or generic conclusions.
One particularly useful approach is to use AI as an editor rather than an author. Let it challenge your arguments, identify weak passages, suggest improvements, and help you express your own ideas more clearly.
And always read the result carefully. Does the argument make sense from beginning to end? Are there contradictions, repetitions, or paragraphs that don't add anything? Are there factual claims that need verification? Does the text actually communicate your ideas, or has the model introduced opinions and conclusions you never intended? Remove unnecessary content, verify the facts, and keep iterating until every paragraph serves a purpose.
Good writing begins with having something worth saying. AI can help you say it better, but it shouldn't replace the thinking behind it.
Web design
AI-generated websites have developed their own recognizable aesthetic.
Ask an AI coding tool to build a landing page, and you will often get a familiar combination of oversized headings, gradient backgrounds, rounded cards, generic icons, and a hero section that looks like a thousand other SaaS websites.
The copy often suffers from the same problem. Headlines like "Unlock the Power of AI," "Transform Your Workflow," or "Take Your Business to the Next Level" might sound professional, but they communicate almost nothing. Entire sections can be filled with marketing language that never explains what the product actually does or why anyone should care.
The result might be responsive, functional, and technically well-built, yet have no visual identity or meaningful message.
To avoid this, approach AI-assisted web design the way a designer would. Start with inspiration from existing websites. Study their typography, spacing, color palettes, composition, navigation, and use of imagery.
More importantly, think about the purpose of the website. Who is the audience? What should visitors notice first? What actions should they take? What impression should the design communicate?
The copy deserves just as much attention as the visuals. Define the message before generating the page. What problem does the product solve? What makes it different? Can a visitor understand its value within a few seconds? Write headlines that say something specific, descriptions that provide useful information, and calls to action that clearly explain what happens when clicked. Avoid filling space with meaningless marketing phrases or unsupported claims.
Instead of asking AI to "build a modern landing page for an analytics product," you could specify an editorial layout with restrained colors, strong typography, generous whitespace, real product screenshots, and a clear hierarchy that prioritizes the product over decorative elements. Provide the actual product description, its main benefits, and the message you want the website to communicate rather than letting the model invent them.
References are especially powerful here. Providing screenshots or an established design system often works better than describing a visual direction through adjectives alone.
And don't stop once the website renders correctly. Inspect individual sections, evaluate spacing and alignment, test different screen sizes, and iterate on the details.
Look closely for inconsistencies. Are the margins and paddings consistent? Do buttons, icons, and typography follow the same design language? Are elements properly aligned? Does the layout break on smaller screens? Are there buttons that don't work, awkward text wrapping, or accessibility issues such as insufficient contrast?
Read through all the copy as well. Does every headline communicate something useful? Are there repetitive sections, vague promises, or sentences that sound impressive but mean nothing? Is the terminology consistent across the website? Does the text match what the product can actually deliver? Go through the website as an actual visitor would, test its functionality, question its messaging, and fix what doesn't feel right. Small inconsistencies accumulate, and they can make an otherwise good website feel unfinished.
A website that works is not necessarily a website that is well designed. Functionality, communication, and taste are different problems, and all deserve attention.
Say no to slop
It's understandable that not everyone has time for research, iterations, and careful prompt engineering in a fast-moving world. AI makes it tempting to take the easiest route and publish whatever comes out. But these easy gains can come at a long-term cost. People notice when something lacks thought or effort, and over time, they become less likely to pay attention to or trust the person behind it.
The consequences go beyond individual creators. Flooding the internet with slop produces enormous amounts of noise, making genuinely valuable content harder to discover. The more we produce without considering quality, the harder it becomes for everyone to find something worth their attention.
AI has made producing content almost effortless. But making something worth consuming still requires thought, intention, and taste. If anything, those qualities have become more valuable than ever.
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