AI Image Prompting Guide 2026: Get Better Results From Every Generator

May 5, 2026 · Editorial Team · 9 min read · tutorial image-generation prompt-engineering

Most people who complain about AI image generators are actually complaining about their prompts. The tools themselves are genuinely capable, Midjourney v7, Flux Pro, and DALL-E 3 can produce professional-quality imagery. But each one has a different prompting logic, and what works brilliantly in one often gets you nothing in another.

This guide covers the actual mechanics: Midjourney's parameter syntax, DALL-E's natural language adherence, Flux and Stable Diffusion's weighting system, negative prompts that work, aspect ratio handling, and how to control style without losing content. It's not a list of example prompts to copy. It's an explanation of why the prompts work, so you can write better ones yourself.

How each generator interprets prompts differently

Before getting into tactics, it's worth understanding what you're working with. These four generators have genuinely different architectures, and the difference shows in how they respond to prompts.

Midjourney uses a proprietary training process with strong aesthetic biases baked in. It interprets prompts loosely, a short evocative phrase often works better than a detailed specification, because the model fills gaps with its aesthetic training. Midjourney is very opinionated about style. If you don't specify one, you'll get the Midjourney look, which is polished and slightly cinematic.

DALL-E 3 (available through ChatGPT and the OpenAI API) is much more literal about prompt adherence. It follows detailed instructions closely, handles text in images better than any other mainstream generator, and pays attention to negations ("a dog without a collar" actually works). The tradeoff is that outputs can feel slightly less painterly than Midjourney. DALL-E 3 also rewrites your prompt before generation, you can disable this via the API but not through ChatGPT.

Flux Pro from Black Forest Labs is the current best open-weights competitor to Midjourney. Flux 1.1 Pro is more literal than Midjourney but less rigid than DALL-E, landing in a middle ground that many photographers and designers prefer. It handles photorealism well and responds well to detailed composition descriptions.

Stable Diffusion (in XL or SDXL form) is the most malleable of the group. With the right fine-tuned model and LoRA adapters, it can match or exceed all the above in specific styles. But base SDXL without fine-tuning isn't as polished, and the prompting conventions it uses (comma-separated tag lists) are different from the natural language the others prefer.

The structure of a strong prompt

A well-crafted image prompt usually has five components. Not every prompt needs all five, but knowing what they are helps you figure out what's missing when a result isn't what you wanted.

Subject. What's in the image? Be specific about the subject itself. "A woman" is vague. "A woman in her 40s with short gray hair and paint-stained hands" gives the model more to work with and reduces the chance of it defaulting to a generic model-stock-photo type.

Action or state. What is the subject doing, and how? "A woman sitting at a cluttered desk" versus "a woman leaning forward, hands folded, in direct eye contact with the viewer" produce very different images. Movement and posture are often under-specified.

Environment and lighting. Where is this scene happening, and what's the light source? "Afternoon light through venetian blinds" and "overcast studio lighting" result in completely different moods even with the same subject. Lighting is arguably more important to the final feel than any other element.

Style or medium. What visual style should the image have? Photorealistic, oil painting, pencil sketch, flat vector illustration, architectural rendering, these all need to be specified. Without a style directive, the generator defaults to its trained aesthetic.

Technical parameters. Aspect ratio, quality settings, version number (in Midjourney), or sampling steps (in Stable Diffusion).

Putting this together: A woman in her 40s with short gray hair and paint-stained hands leaning forward over a cluttered studio desk, afternoon light through dusty venetian blinds, photorealistic, detailed, 35mm lens, shot on a Sony A7R IV --ar 16:9 --style raw is a stronger prompt than a woman at a desk.

Midjourney: syntax that actually matters

Midjourney uses --parameter value syntax appended at the end of your prompt. The parameters that most affect output quality:

--ar sets aspect ratio. --ar 16:9 for landscape, --ar 9:16 for portrait/mobile, --ar 1:1 for square. This affects composition significantly, don't skip it.

--style raw disables Midjourney's automatic aesthetic enhancement. The default style adds polish and a specific cinematic look. Raw mode produces more neutral output that's closer to what your prompt literally described. If your results always look like Midjourney images no matter what you write, try --style raw.

--stylize (abbreviated --s) controls how strongly Midjourney applies its aesthetic preferences. The default is 100. Lower values (25-50) produce results that stick closer to your prompt. Higher values (250-1000) give Midjourney more creative latitude. If your prompts feel ignored, lower the stylize value.

--no is the negative prompt equivalent. --no text, watermarks, blurry tells the model what to exclude. It works but isn't as reliable as Stable Diffusion's dedicated negative prompt field.

--seed sets the seed for reproducibility. Once you have a composition you like, note the seed (visible in the job URL). Use the same seed with small prompt variations to iterate without changing the entire composition.

--cref enables character reference, point it at an existing Midjourney image URL and subsequent generations will try to maintain that character's appearance. Useful for maintaining consistency across a series.

For style matching, Midjourney now supports --sref (style reference) pointing at any image URL. If you have a specific visual style you want to replicate, this is more reliable than describing the style in words.

DALL-E: working with its literalism

DALL-E 3's strength is following instructions accurately. Its weakness is that it can produce outputs that feel slightly over-processed when you're looking for photographic naturalism. A few prompting approaches that help:

Use negations freely. "A modern kitchen with no appliances visible" works. So does "a street scene with no text or signage." DALL-E follows negations more reliably than most generators.

Specify text explicitly. DALL-E handles text in images better than any other mainstream model. If you need a sign, label, or caption in the image, spell it out exactly: a storefront with a hand-painted wooden sign reading "Open for Business". The text will be legible and accurate.

Give it camera and lens details. DALL-E responds well to photography-style descriptions. "Shot on a 50mm prime lens with shallow depth of field, subject in sharp focus with soft bokeh background" produces more controlled photorealistic results than just saying "photorealistic."

Work with the system prompt rewriting. When used through ChatGPT, DALL-E 3 rewrites your prompt before generation. This usually improves vague prompts but can change specific requests. If your prompt keeps being modified in ways you don't want, use the API directly with quality: "hd" and set the prompt exactly as you want it without the rewriting layer.

Flux and Stable Diffusion: weights and structure

Flux and Stable Diffusion (especially in SDXL variants) share some prompting conventions that differ from the natural-language approach of Midjourney and DALL-E.

Prompt weighting lets you emphasize specific terms. The syntax varies by implementation, but the common formats are (term:weight) or [term] for de-emphasis. For example: (golden hour lighting:1.5), professional photography, outdoor portrait emphasizes the lighting more than the photography style. Don't over-weight, values above 1.8 tend to produce artifacts. Weights between 1.1 and 1.5 are the useful range.

Tag-based prompting works well for Stable Diffusion models. Instead of writing a sentence, list descriptive tags separated by commas: photorealistic, 8k, professional portrait, soft studio lighting, shallow depth of field, Canon EOS R5. This reflects how most SD training captions were structured, so the model responds well to it.

Negative prompts matter more here. A standard negative prompt for photorealistic SD/Flux work: blurry, low quality, jpeg artifacts, watermark, signature, text, deformed, extra limbs, extra fingers, distorted, oversaturated. This removes the most common artifacts and quality issues without restricting the subject matter.

CFG scale (guidance scale) in Stable Diffusion controls how closely the model follows the prompt. Lower values (4-6) give the model more creative freedom. Higher values (8-12) force it to stick closely to the prompt but can produce over-sharpened, artifact-prone results. Most workflows work well at 7-9.

Negative prompts: what works and what doesn't

Negative prompts tell the generator what to avoid. They're most powerful in Stable Diffusion-based tools and less reliable in Midjourney. Here's what actually helps:

Quality artifacts to always exclude: blurry, low quality, low resolution, pixelated, jpeg artifacts, overexposed, underexposed, flat lighting

Anatomy artifacts (especially for human subjects): deformed hands, extra fingers, malformed limbs, bad anatomy, distorted face, asymmetrical eyes

Compositional issues: text, watermark, signature, frame, border, logo

Style contamination: If you're going for photorealism and keep getting painterly results, add painting, illustration, drawing, cartoon, anime, digital art to your negative prompt.

One thing to avoid: extremely long negative prompts with 30+ terms. There's a diminishing return, and past a certain length, the negative prompt starts working against your positive prompt by pulling the model away from good output directions as well as bad ones. Keep negative prompts focused on the specific problems you're actually seeing.

Aspect ratio and composition planning

Aspect ratio affects more than just the dimensions. A portrait orientation (9:16) naturally pushes the model toward vertical compositions. Landscape (16:9) favors horizontal framing. Square (1:1) typically centers the subject. Think about your intended use case before generating:

Social media portrait: --ar 9:16 or 4:5
Website hero image: --ar 16:9 or 2:1
Product shot: --ar 1:1 or 4:5
Editorial/print: --ar 3:2 or 4:3

For more detail on which ratio to use for which platform and tool, see AI image aspect ratios explained.

Style control without losing content

Getting style right without losing the subject is the hardest part of image prompting. A few techniques that work:

Front-load content, end with style. Put subject description first and style descriptors last. A weathered fisherman repairing a net at sunset, oil painting in the style of Winslow Homer, warm earth tones, textured brushwork keeps the content clear while the style modifiers build on it.

Use medium-specific vocabulary. "Impressionist" is vague. "Loose impressionist brushstrokes, visible paint texture, dappled light, palette knife technique" tells the model exactly what kind of impressionism you mean.

Reference specific photographers for realism. "[Subject], shot in the style of Sebastiao Salgado, black and white, high contrast, dramatic lighting" is more precise than "documentary style photograph." Most image generators have absorbed photographic styles and respond well to specific photographer references.

Krea AI offers real-time style adjustment, which is useful for iterating style without re-running full generation cycles. If you're doing a lot of style exploration, it saves significant time.

A workflow that actually works

Rather than iterating on one prompt forever, use this structure:

Start with a sparse prompt to establish subject and composition. Don't add style yet. Confirm the model has the right subject in the right setting.
Fix composition. Use aspect ratio and explicit framing directions ("close-up portrait, shallow depth of field") to get the composition right before adding style complexity.
Add lighting and style. Once the composition is working, layer in lighting descriptors and style modifiers. Evaluate whether they're working or overwhelming the composition.
Use seeds to lock good compositions. When a composition works, save the seed. Then iterate on style, lighting, and detail while keeping the composition stable.
Post-process with Topaz Labs or Magnific for upscaling. Don't fight the generator to produce 4K detail, generate at a good size, confirm it works, then upscale.

Most failed prompting sessions try to get everything right in one generation. The iterative approach, composition first, then lighting, then style, then detail, produces better results with fewer frustrating regenerations.