How to Create AI Images?

Why AI Image Generation Feels Harder Than It Should

AI image tools are everywhere — yet most people still get wildly inconsistent results. You type a prompt, cross your fingers, and hope the fourth variation looks something like what you imagined. That's not a skill problem; it's a process problem.

The honest truth is that even professionals grind through this. Professional photographer Darren Rowse, who brings 40 years of photographic experience to AI, documented that a single Midjourney series took "hundreds and hundreds of iterations and many hours" — sometimes up to 100 iterations for one image, spanning multiple days (https://digital-photography-school.com/ai-images/). As he puts it, working with AI is "not just about plugging in a few words and watching as the program spits out great photos."

If a seasoned pro needs that many rounds, your frustration isn't a personal failing — it's evidence that the guesswork approach doesn't work. What does work is a repeatable workflow:

• Choosing the right tool for your specific use case, not the trendiest one
• Writing short, clear prompts instead of long, clever ones
• Iterating with small, targeted revisions — one element at a time
• Finishing and publishing the final file deliberately

Here's why this matters now more than ever. AI image generation has moved well beyond novelty status: market research values the AI image generator market at USD 474.2 million in 2025, projected to reach USD 1,853.0 million by 2033 — an 18.8% annual growth rate. The software segment alone accounts for 78.0% of revenue, and tools are being embedded directly into design suites, office apps, and CMS platforms so users can "generate, edit, and refine visuals within their existing workflows."

Major brands confirm the shift. According to Google's published use cases, companies like WPP and Authentic Brands Group now turn a single creative hypothesis into "hundreds or even thousands of personalized, cinematic creative variations in hours."

In other words, AI image generation is now a mainstream business skill — one worth learning systematically rather than through trial and error. The good news: the process follows a repeatable pattern, and the repetitive parts (batch generation, file handling, publishing) are exactly the kind of work that agent skills can automate later — SkillzBench is building a free, verified directory of those skills, along with a free MCP connector, to help you go beyond the manual workflow.

Step 1: Pick the Right Tool for Your Use Case

Search "best AI image generator" and you'll find dozens of tools, each claiming to be the one. The truth is simpler: there is no single best tool, only the right tool for your specific job. Match the tool to the use case first, and everything downstream — prompting, iterating, exporting — gets easier.

Three tools cover most needs. For ease of use and images containing readable text, ChatGPT is the clear starting point. You simply ask in chat, an image generates in about a minute, and edits happen conversationally. Its text rendering is a genuine differentiator: hands-on testing by Zapier reports GPT Image 2 achieving roughly 99% text accuracy with the standard Latin alphabet, while Midjourney can't reliably render text at all.

For creative and aesthetic work, comparisons consistently call Midjourney v7 "the gold standard for aesthetic quality." The trade-off is control versus convenience. As Zapier puts it: with ChatGPT you get great results but limited shaping; with Midjourney you do more work but gain far more control. Plans run from Basic at $10/month (~200 generations) to Pro at $60/month, which also unlocks Stealth Mode — worth knowing because Midjourney publishes your images to a public member gallery by default.

For free or self-hosted workflows, two options stand out:

• Flux 2.0 — the strongest open-source model available, runs on consumer GPUs, and supports LoRA fine-tuning for custom brand styles.
• Stable Diffusion XL (SDXL) — free open weights and an unmatched ecosystem of fine-tuned checkpoints on Civitai.
• ChatGPT — free tier with limits, or Plus at $20/month for heavier use.

Resolution matters too: Midjourney v7, DALL-E 4, and Flux 2.0 support up to 4K, while SDXL tops out around 2K.

Here's a quick mapping to get you started:

One caution before you commit: the U.S. Copyright Office has held that purely AI-generated images can't be copyrighted, which matters if you're building brand assets. And if your workflow involves repetitive steps around image generation — batch prompts, file handling, publishing — agent skills can automate much of that. SkillzBench is building a free directory where you'll be able to browse, verify, and test those skills, with a free MCP connector to plug them into your agents.

Step 2: Write Prompts That Work — Short, Clear, Specific

The difference between a mediocre AI image and a great one usually comes down to a few well-chosen words. The counterintuitive truth, confirmed by OpenAI's official guidance, is that short, specific prompts beat long, clever ones — in most cases, 1–3 clear sentences are enough.

According to OpenAI's image generation guidance, an effective prompt covers five things: the purpose of the image, the main subject, the action, the setting, and the desired visual style. Just as important is how you describe those elements. OpenAI puts it plainly: clarity is more effective than clever phrasing, especially for details like layout, texture, materials, and light. "Soft natural light from a window on the left" will outperform "beautiful lighting" every time.

Word order matters more than most beginners realize. Testing by a professional photographer found that rearranging the words in a prompt significantly changes the output, so treat the sequence of your terms as part of the instruction, not just the vocabulary.

If your image needs to contain readable text, prompt construction becomes even more deliberate:

• Put the exact text in quotes or ALL CAPS so the model treats it as literal content
• Specify the font, size, color, and placement
• Keep text short — fewer words means fewer rendering errors
• Spell out brand names letter-by-letter, such as "S-T-R-I-P-E"

These techniques matter because text accuracy varies sharply between tools — one hands-on comparison reported roughly 99% text accuracy for GPT Image 2 with the standard Latin alphabet, while Midjourney can't reliably render text at all.

There's also an edge worth exploiting if you come from a visual background. Photographer Darren Rowse, drawing on 40 years of experience, uses vocabulary from his own domain — photographic lighting terms and camera settings — to tell the AI precisely what he wants, alongside mood and atmosphere words. If you know what "backlit," "shallow depth of field," or "golden hour" actually look like, you already have a professional prompt vocabulary most users lack.

One final principle to carry into the next stage: write your prompt to get the core idea right first, then plan to refine one element at a time. The prompt isn't a one-shot magic spell — it's the starting point of an iterative conversation. And if you'd rather not handle the repetitive parts of that workflow manually, agent skills can automate steps like batch generation and file handling; SkillzBench is building a free directory where you can browse and test skills before connecting them to your agent.

Step 3: Generate, Iterate One Change at a Time, and Finish the File

Your first generation is rarely your final image — and that's by design. OpenAI's official guidance says the best way to improve an image is through small, targeted revisions, adjusting one element at a time rather than rewriting the whole prompt (OpenAI Academy).

The practical loop looks like this: generate, review, then issue an edit prompt like "Change only the lighting. Keep everything else exactly the same." This keeps the model from reinterpreting parts of the image you already like. It also helps to repeat important details — subject, style, lighting — in each revision, because step-by-step edits can otherwise cause the image to drift away from your original vision.

In Midjourney, the rhythm is slightly different: you enter a prompt, receive four options, then use Conversational Mode for quick tweaks or advanced options for bigger structural changes. Professional photographer Darren Rowse's documented workflow shows how far iteration can go — a single image series took "hundreds and hundreds of iterations and many hours," sometimes up to 100 rounds for one image, spread across multiple days (Digital Photography School).

A few techniques worth building into your routine:

• Reorder or add words in the prompt when a change won't take — word order measurably affects output.
• Upload your own reference photos to steer lighting and composition, and run your best results back through the tool to teach it what you like.
• For multi-image scenes, refer to references by order and use spatial language like left, right, foreground, and background.
• Only ask for one change per edit — stacking requests invites unwanted side effects.

Once the image looks right, finish the file. Rowse does minor Photoshop retouching — healing blemishes and cleaning artifacts — then uses Topaz AI software for upscaling, sharpening, and denoising before export. For text-heavy layouts like infographics, OpenAI recommends emphasizing sharp text rendering in the prompt and polishing the result in a design tool afterward.

Two quick cautions before you publish. First, under the U.S. Copyright Office's February 2023 ruling (reaffirmed since), purely AI-generated images cannot be copyrighted, so don't build a brand asset strategy around exclusive rights to raw outputs. Second, Midjourney publishes your images to a public member gallery by default — only the $60/month Pro plan's Stealth Mode keeps them private.

If this generate-refine-export loop becomes part of your regular workflow, the repetitive steps — batch generation, file handling, publishing — are exactly the kind of work agent skills can automate. SkillzBench is building a free directory of vetted skills plus a free MCP connector so you can wire the ones you choose into your agents; join the waitlist to be first in line.

CtaText: See which agent skills actually work — browse and test free on SkillzBench SocialProofText: Every skill version-pinned, security-scanned, and benchmarked with published Lift scores — free, always.

Automate the Repetitive Parts with Agent Skills

By the time your prompt is polished and the image looks right, the creative part is over — but the work isn't. Renaming files, resizing for each platform, and publishing across channels can quietly eat more time than the generation itself.

Consider what the manual workflow actually involves. A professional photographer documenting his process reported that a single image series took "hundreds and hundreds of iterations and many hours" — and that was just getting the picture right, before any post-processing or publishing began (Digital Photography School). OpenAI's own guidance reinforces that quality comes from small, targeted revisions, adjusting one element at a time (OpenAI Academy). That iteration-heavy process produces a steady stream of files that all need the same finishing treatment.

The good news: those finishing steps are the most predictable part of the workflow, which makes them ideal candidates for automation. Agent skills — SKILL.md instruction packages your coding agent can follow — can hand off tasks like:

• Batch-generating image variations from a list of prompts, so you get dozens of options in one run instead of clicking through one image at a time
• Handling files automatically: consistent naming, folder organization, and format conversion
• Resizing and upscaling each image to the dimensions each platform requires
• Publishing finished images to social channels with captions, on a schedule

This is how enterprises already operate at scale. Brands like WPP use models like Imagen 4 to turn a single creative hypothesis into "hundreds or even thousands of personalized, cinematic creative variations in hours" (Google Cloud). The same principle applies at any size: let the agent handle the repetitive volume while you focus on the creative judgment calls.

The catch is knowing which skills to trust. Published research found that roughly 26% of agent skills carry at least one vulnerability, so grabbing random packages from GitHub is a genuine risk. That's the problem SkillzBench is building against: a free, independent directory that continuously crawls and dedupes skills from GitHub, pins every version to a commit SHA, runs security scans and license checks, and publishes a trust score from 0 to 100. Every skill is benchmarked in a sandbox — tested with and without the skill, using the same model — so you can see the measured Lift in percentage points before you install anything.

SkillzBench is also building a free MCP connector that lets you toggle exactly which skills and tools each of your agents can see, per agent. The product is pre-launch, but the waitlist is open — join it, and when the directory and connector go live, you'll be able to wire your image workflow end to end: prompt in, finished and published files out, with the repetitive middle handled by skills you've verified yourself.