Most challenge guides skip the part that determines results: the anatomy of a prompt. Here’s the framework that separates good entries from great ones — with worked examples and this week’s theme.
- The 6-part prompt formula that consistently produces standout image AI results (with color-coded breakdown)
- Bad-to-good rewrites for three common prompt mistakes
- The key Midjourney V7 parameters that most people ignore
- This week’s challenge themes + starter prompts for each
- The one thing people get wrong when evaluating their own outputs
Spent a while looking at AI image challenge submissions across Reddit, Discord, and community galleries. And honestly? Most entries at the beginner-to-intermediate level hit a ceiling that has nothing to do with artistic vision. Same types of mistakes, over and over. The prompt is either too vague (“a beautiful landscape”) or over-stuffed with nouns that fight each other (“futuristic neon cyberpunk magical forest ethereal glowing”). Neither approach tells the model what it’s actually looking at.
The people whose work consistently stands out are doing something specific with their prompts. Not longer, not more creative. More structured. They’re treating the prompt like a brief, not a wish. Subject. Medium. Lighting. Composition. Mood. Parameters. In that order, roughly.
That’s the whole playbook. Everything below is unpacking it.
“V6 wants natural language, not keyword lists. Writing it like a photography brief changed everything.”
r/midjourney, u/photoreal_mj (2,100 upvotes, 2025) — via AI Tool Discovery community roundupMidjourney V7 (default model as of Q3 2025, per Printify’s January 2026 guide) processes prompts differently than earlier versions — it responds better to natural language and gives more weight to words at the beginning of the prompt. Which means word order matters. And it means the “dump everything at once” approach actively works against you.
Here’s the formula. Color-coded so you can see what each part is doing:
Three worked rewrites. Each one isolates a specific mistake and shows the fix. These come from public challenge submissions analyzed in community galleries — the types of mistakes are broadly representative, not cherry-picked edge cases.
Rewrite 02 — The Noun Pile
Rewrite 03 — The Missing Parameters
--chaos 15 to introduce controlled unpredictability in the nebula detail. The astronaut now has a spatial relationship with an object (nebula) and a light source (off-frame star).
The Parameters Most People Skip
Parameters go at the end of the prompt, after everything else. They don’t change what the model generates — they change how it generates it. Aspect ratio is the one most people know. Three others that noticeably improve outputs when used correctly:
| Parameter | What it does | Example value | ⚠ Misuse |
|---|---|---|---|
| –ar | Aspect ratio — sets image dimensions. Default 1:1 (square). | –ar 16:9 (landscape) / –ar 9:16 (portrait/mobile) / –ar 2:3 (portrait print) | Forgetting this means all outputs are square. Wrong format for most publishing contexts. |
| –stylize (–s) | How much Midjourney’s aesthetic training influences the result. Range 0–1000; default 100. | –s 200 (moderate art influence) / –s 600 (strong stylization) / –s 0 (literal, no artistic interpretation) | High –stylize can override your style specification in the text prompt. Test at multiple values. |
| –chaos (–c) | How varied the four output options are. Range 0–100; default 0. | –c 10 (subtle variation) / –c 40 (wider exploration) / –c 0 (consistent outputs) | High chaos makes outputs unpredictable and hard to iterate on. Good for ideation; bad for refining a direction you’ve found. |
| –sref [URL] | Style Reference — forces the model to apply the visual aesthetic of a reference image. Introduced in V6, refined in V7 per Printify, Jan 2026. | –sref https://yourimage.com/reference.jpg –sw 1000 | Only works with a valid image URL. –sw (style weight) controls how strongly the reference is applied; too high can swamp your text prompt entirely. |
| –no | Negative prompt — explicitly exclude elements. | –no text, watermark, blurry, oversaturated | Less powerful than Stable Diffusion’s negative prompt system. Some elements (text artifacts, extra limbs) still appear despite exclusion. Helps but doesn’t guarantee. |
Why most people can’t tell if their prompt actually improved: the four-image output grid Midjourney returns is both the model’s attempt and its confidence distribution. When all four look similar and mediocre, the problem is usually an overly specific prompt that left the model no room for interpretation. When they all look wildly different and none is quite right, the problem is usually an insufficiently constrained prompt. The pattern of variation across the grid is diagnostic information — most people only look at whether any individual output is good, not what the distribution of outputs tells them about the prompt. A high-chaos, low-specificity prompt produces wide variation; a tightly-specified, high-stylize prompt produces narrow but aesthetically interpretive variation. Those are different problems with different fixes.
This Week’s Challenge Themes
Four themes. Each one has a starter prompt — use it directly or pull it apart and rebuild with your own direction. The starter prompts are intentionally specific; feel free to replace any element. That’s how you learn faster than reading about it.
elevated megacity at night, vertical gardens cascading down brutalist towers, bioluminescent river below, rain-slicked streets, long exposure light trails, blade runner meets Miyazaki, cinematic widescreen --ar 21:9 --stylize 500
bold 1960s pop art poster, close-up of a vintage rotary phone, flat graphic style, primary red and yellow with heavy black outlines, halftone dot texture, Roy Lichtenstein influence --ar 2:3 --stylize 300 --no photography, realism
ancient sea serpent emerging from an arctic fjord at blue hour, covered in barnacles and bioluminescent scales, traditional woodblock print style, muted indigo and silver palette, dramatic upward perspective --ar 9:16 --s 400
grief as a flooded room, personal objects floating, overcast light through a water-stained window, desaturated palette with single warm lamp glow in distance, painterly oil texture, intimate and still --ar 3:2 --stylize 600 --c 5
Ready to submit your creation?
Post your image on social media and tag the challenge. The community votes on standout entries each Friday.
Join the Challenge #AIImageChallenge · #BestPromptArtTools You Can Start With Today
Not a comprehensive review — see the full prompt tools guide for that. These three cover the most common entry points for challenge participants:
V7 default since Q3 2025. Strongest for photorealism and artistic coherence. Basic tier $10/month (200 images). Pricing: midjourney.com — verify before purchasing.
Free, fully local, more control than Midjourney but steeper learning curve. Negative prompts are more powerful here. No subscription required for local use.
Strongest for instruction-following and text in images. Less aesthetic control than Midjourney for artistic styles. Included in ChatGPT Plus.
Cross-source synthesis — not visible in any single cited source
Three sources — Midjourney’s community data on V6/V7 output patterns, the prompt framework research on what produces variation vs. consistency, and the parameter behavior documentation — point to a finding that none of them states directly: the most common reason challenge entries look like AI-generated content rather than creative work is that the prompt describes a desired feeling rather than a specific visual scene. Words like “beautiful,” “epic,” “magical,” and “ethereal” are emotional targets, not visual inputs. The model has no rendering pathway for them except generic associations from training data. When you replace those words with specific visual ingredients — lighting angles, material textures, spatial relationships, color palette ranges — the output stops looking like what a committee designed and starts looking like what a photographer or illustrator intentionally constructed.
That shift, from emotional description to visual specification, is the single highest-leverage skill in image AI prompting. It’s not about longer prompts. It’s about the right kind of specificity.
Start narrow. The blank canvas is a trap.
Look, here’s what this actually is for you: the hardest part of a blank prompt box is that it feels like anything is possible, which makes it hard to start. The creative freedom is real but it’s not how good prompts get written. Good prompts start from constraints. Pick one of the four challenge themes above, pick one of the starter prompts, and change only one element. The medium, the lighting, or the aspect ratio. Generate that. Then change one more thing. You’ll understand more about how the model responds from three iterations than from reading a guide for an hour.
What you do: the beginner formula that consistently produces usable outputs is Subject + Medium + Lighting + Aspect Ratio. Four elements. That’s it. A lone lighthouse, acrylic impasto style, dramatic storm lighting, –ar 16:9. Short, specific, visually coherent. Add more later when you know what you’re working with.
Here’s what’s going to stop you: the temptation to add more words when the first output isn’t what you wanted. Longer prompts don’t reliably produce better outputs. The issue is usually specificity in the existing elements, not the count of elements. Before adding a new element, ask whether you’ve actually specified the existing ones. What kind of lighting? What angle of composition? What material or style reference?
Stop doing this: don’t use emotional adjectives as the primary descriptors — “beautiful,” “amazing,” “epic,” “stunning.” Those are how you feel about the image, not visual ingredients the model can render. Replace each one with a specific visual equivalent before generating.
The output grid is telling you something. Are you listening to it?
Look, here’s what separates mid-tier from standout entries: most intermediate participants pick the best output from the grid and submit it. The participants whose work consistently gets noticed are using the grid as diagnostic feedback on the prompt itself, then iterating before committing. When all four outputs look similar and mediocre, that’s an over-specified prompt. When they all look radically different and none lands, that’s an under-constrained prompt. Different problems, different fixes.
What you do: use –chaos intentionally, not by default. Start at –c 0 with a specific prompt to get consistent outputs you can refine. Once you’ve found a direction you like, bump to –c 10–20 to explore variations without losing the direction entirely. The –sref parameter is underused by intermediate participants. If you have a reference image that captures the visual aesthetic you’re chasing, –sref [URL] will get you closer faster than trying to describe that aesthetic in words.
Here’s what’s going to stop you: the submission timing. Challenge winners tend to submit within the first 48 hours while visibility is highest in the community feed. Not because early submissions are better — but because they accumulate more engagement, which affects how they’re surfaced. Your best technical work submitted late can underperform a slightly weaker entry submitted early. Submit your strongest first-draft work early, then refine for the following week.
Stop doing this: don’t use V7 prompts structured like V5 keyword lists. The model changed. Per community testing documented in r/midjourney, V6 and V7 respond better to natural language than to comma-separated noun lists. “A woman walking through rain in a neon-lit Tokyo street, photorealistic, cinematic” outperforms “woman rain Tokyo neon photorealistic cinematic.” Different prompt structure, same elements, better output.
