How to Create AI Generated Images?

Why AI Image Generation Feels Harder Than It Should

You type a detailed description into an AI image generator, hit enter, and get back something generic that looks nothing like the picture in your head. If that keeps happening to you, you're in good company — and the problem probably isn't the tool.

AI image generation has gone mainstream at remarkable speed. More than 150 million people now use AI image generators every month, and OpenAI reported over 130 million people and 700 million images in the first week after ChatGPT launched native image generation, according to industry statistics tracking. Access isn't the barrier anymore. Getting results that match your vision is.

G2 puts it bluntly in its head-to-head comparison: "Creating AI images is not as simple as it looks." The same review notes that both major tools "reward thoughtful, detailed prompting" — meaning the gap between a disappointing image and a great one usually sits in the prompt, not the platform. Leonardo.Ai's own prompting guide describes the exact frustration: you have a clear vision, but the model "returns something generic or very far from what you have in mind," pushing many people to guess at "magic words" or give up entirely.

The good news is that prompt failure is surprisingly predictable. The same guide documents four recurring mistakes that hold beginners back:

• Vagueness — the prompt describes a general idea ("a nice landscape") instead of a specific scene, so the model fills the gaps with its own defaults.
• Indecisiveness — hedging or stacking multiple competing directions in one prompt, leaving the model to pick a compromise you never asked for.
• Over-complexity — cramming too many elements into a single pass instead of focusing on 3–5 core elements and building complexity iteratively.
• Conversational language — talking to the image model like a chatbot, when these systems are pattern-matching engines that read prompts as descriptive commands, not dialogue.

There's also a tool-selection trap. CNET's hands-on testing found no single winner across the board: Google's Nano Banana 2/Pro scored highest overall at 8.5, while Canva Magic Media ranked best for beginners, Adobe Firefly for professionals, and Midjourney for creative mood — each suited to a different use case. Picking a tool that doesn't fit your goal makes good results feel even further away.

The fix, then, is learnable craft: a structured prompt framework, awareness of these four failure modes, and a tool matched to what you're actually trying to make. The rest of this guide walks through each step in order — and once your image workflow is running, agent skills can automate the surrounding busywork like batch prompting and asset pipelines; SkillzBench is building a free directory and MCP connector to help you find and test them.

The Subject-Context-Style Prompt Framework (and What Breaks It)

Most disappointing AI images trace back to a prompt that never told the model what it actually wanted. Leonardo.Ai's prompting guide reduces every strong prompt to three parts — subject, context, style — and documents the four ways writers break it.

Start with the subject: the main thing you want to see. Context sets the scene, environment, and mood around it. Style defines the medium, lighting, and aesthetic. A weak version reads "a dog." A stronger one reads "a golden retriever puppy, subject; sitting on a rain-soaked city sidewalk at dusk, context; cinematic photograph, shallow depth of field, warm streetlight glow, style." According to G2's head-to-head comparison, both Midjourney and DALL·E 3 "reward thoughtful, detailed prompting" — the better the input, the better the output on either side.

Four failure modes break the framework, and each has a fix:

• Vagueness — "a nice landscape" gives the model nothing to pattern-match. Rewrite with specific nouns, times of day, and named visual qualities.
• Indecisiveness — offering the model a menu ("a cat or a dog, maybe watercolor or oil") forces it to guess. Commit to one direction per generation.
• Over-complexity — stacking ten competing elements dilutes all of them. Leonardo.Ai recommends focusing on 3–5 core elements and building complexity iteratively.
• Conversational language — these models are pattern-matching systems, not chat partners. Write descriptive commands, not requests like "could you maybe make me..."

Aspect ratio is a practical decision, not an afterthought. Leonardo.Ai maps 1:1 to Instagram posts, 16:9 to video thumbnails, and 9:16 to phone wallpapers — picking the ratio before you write keeps composition prompts honest.

Negative prompts deserve a caveat. Leonardo.Ai describes them as soft constraints, not absolute commands: they lower the weight of unwanted terms rather than banning them, because models understand what things are far better than what they are not. The guide's own recommendation is to try positive phrasing first — describe the image you want instead of the one you don't.

For readers who want to automate the surrounding workflow — batch prompt generation, asset renaming, posting pipelines — agent skills can handle that layer. SkillzBench is building a free directory and MCP connector to browse, verify, and benchmark skills before you trust them in your stack; join the waitlist at skillzbench.com.

Picking the Right Tool for Your Use Case

Ask ten people for the "best" AI image generator and you'll get ten different answers — because there isn't one. The right question isn't "which tool is best?" but "which tool is best for what I'm actually trying to do?"

The clearest way to decide is with a decision matrix rather than a single ranking. CNET's hands-on testing scores Google's Nano Banana 2/Pro highest overall at 8.5, but the same review names Adobe Firefly best for professionals (8.0), Canva Magic Media best for beginners (7.5), Stable Diffusion best open-source option (7.0), ChatGPT Images best for existing ChatGPT users (7.0), and Midjourney the most creative pick (6.5). Each winner is only a winner inside its use case.

G2's head-to-head comparison adds a second, complementary axis: mood versus precision. Their verdict — use Midjourney when you want mood and creativity, use DALL·E 3 when you want speed and precision — captures the trade-off most teams face. Midjourney delivers artistic, storytelling-driven visuals; DALL·E 3 follows prompts more literally and is easier to access through a public API. Both, notably, reward detailed prompting equally.

Budget matters just as much as output style. Subscription entry points cluster between $10 and $20 per month: Firefly Standard at $9.99, Midjourney Basic at $10 (paid-only, no free tier), and ChatGPT Plus at $20 with image generation included. If you're building at scale, API pricing changes the math entirely — commercial image generation starts around $0.02 per image, and per-1,000-image costs range from about $10 for Meta's Muse Image to roughly $210 for OpenAI's top model, a 21x spread that has nothing to do with which model tops the quality leaderboards.

A few questions will point you to the right tool:

• Are you a beginner who wants results inside a design app you already use? Canva's 7.5 score and $13/month entry point make it the low-friction choice.
• Do you need commercial, brand-safe output in a professional workflow? Firefly's 8.0 score and Adobe ecosystem integration fit.
• Is artistic, editorial-quality imagery the goal? Midjourney leads on creativity even with the lowest tested score.
• Building image generation into a product? Prioritize public API access and per-image cost over headline quality rankings.

One more consideration: most professionals don't pick just one. Sixty percent of creators use more than one AI tool, with editing and enhancement (55%) actually outpacing generating new assets (52%) as the top use case. Treat tool selection as assembling a small stack matched to your workflow, not crowning a single champion. And once you've chosen your tools, agent skills can automate the surrounding workflow — batch prompting, asset renaming, publishing pipelines — which is exactly the kind of skill you can browse and test for free on SkillzBench's directory as it builds toward launch.

Handling the Hard Stuff: Realistic People, Text, and Quality Control

Two failure modes trip up almost everyone who moves past basic AI image generation: faces that look airbrushed and plastic, and text that emerges as gibberish. The good news is that both have well-documented fixes.

The plastic face problem has a known root cause. According to Leonardo.Ai's prompting guide, the issue "arises from the training data of most models, which can be saturated with heavily retouched, stylized, and idealized photographs" — so the model has essentially learned that flawless equals realistic. The countermeasure is to prompt for imperfection explicitly: ask for detailed skin texture, visible pores, freckles, slight blemishes, and laugh lines around the eyes. Adding a catchlight to the eyes is another documented fix, since reflective, lifelike eyes are a hallmark of convincing portraits.

Independent testing backs this up. A head-to-head comparison by G2 found Midjourney beat DALL·E 3 on realistic skin texture and reflective eyes, while DALL·E 3 faces looked "too smooth" and "uncanny" — evidence that skin realism is a genuine differentiator between tools, not just a prompting problem.

Text rendering is the second pitfall. Leonardo.Ai calls it "one of the most significant challenges for most text-to-image AI models," and prescribes concrete rules that work across tools:

• Put exact text in quotation marks so the model knows what to render
• Keep text under 25 characters — short phrases, not sentences
• Describe the font style (bold, hand-painted, serif) rather than naming a specific font
• Avoid complex typography like overlapping or curved layouts

Tool choice matters here too. CNET's hands-on testing found that Google's Nano Banana "knocks all the other models out of the park, particularly with its ability to create photorealistic images and include legible text" — which is why it earned CNET's best-overall score of 8.5. If your project depends on readable text, that's the tool to reach for first.

For quality control generally, expect quirks rather than perfection. CNET notes that image "hallucinations" show up as flaws you never requested — people with three arms, disappearing elements — and recommends choosing tools that let you fix errors when they inevitably appear. A practical workflow: generate, inspect hands, eyes, and any rendered text first, then regenerate or edit rather than settling.

If you're producing images at any volume, the surrounding workflow — batching prompts, organizing outputs, publishing — is exactly the kind of repetitive work agent skills can automate. SkillzBench is building a free, independently verified directory of agent skills plus a free MCP connector, so you can browse and test which ones actually help before adding them to your stack.

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Compliance, Disclosure, and a Statistics Reality Check

You've crafted the perfect prompt, the image looks great, and you're ready to publish — but have you checked whether you're allowed to use it, and whether you should tell anyone how it was made? Compliance and disclosure are the least glamorous part of AI image creation, and the part most people skip.

Start with the practical basics. Reviewers at CNET advise checking each tool's training and privacy policies before use, and disclosing your AI use when posting — especially since photorealistic outputs are now "scarily realistic-looking." That disclosure habit matters more as tools improve.

The legal picture is murkier than most creators assume. The US Copyright Office has stated that a prompt alone is insufficient for copyright protection, though protection may extend to expressive elements determined by a human author. G2's comparison of Midjourney and DALL·E 3 reaches a similar conclusion from the reviewer's side: there's still no clear legal stance on copyright ownership of AI-generated content, so treat high-stakes commercial projects with extra caution.

If you publish to European audiences, new duties apply. Under the EU AI Act, Article 50(2) requires providers to mark synthetic content in machine-readable form, and Article 50(4) requires deployers to disclose deepfakes. Prohibitions effective December 2, 2026 also cover non-consensual explicit depictions of identifiable people.

Before you hit publish, run through this checklist:

• Review the tool's training data and privacy policy before uploading sensitive or branded material.
• Disclose AI use when publishing images, particularly photorealistic ones.
• Assume a prompt alone gives you no US copyright protection on the output.
• Check EU AI Act Article 50 marking and deepfake-disclosure obligations if you serve EU users.
• Remember that C2PA provenance records origin and edits but does not prove depicted content is true.

A sidebar on statistics: don't repeat numbers you can't trace. The famous "15 billion AI images" figure traces to a frozen August 15, 2023 Everypixel estimate — and downstream articles have fabricated "updates" the original source never published. Market-size forecasts are just as unreliable: for the same 2026 market, analysts report estimates of $484.29 million, $555.1 million, and $15.18 billion, figures gradually.ai warns lack a shared market definition or methodology. As DigitalApplied puts it, any figure you cannot trace to a dated primary source should be assumed fabricated. That skepticism is the same muscle we're building at SkillzBench, where our upcoming verification and benchmarking work will pin claims to checkable evidence — and where free agent skills can eventually automate the disclosure and publishing steps of your image workflow.