Yesterday someone shipped a tool that changes how you approach GPT Image 2. The twist: it does not just give you better prompts. It tells you exactly why your current ones are broken.
What changed with GPT Image 2
The model dropped two weeks ago and it is genuinely a step change in quality. But it punishes vague prompting harder than any image model before it. The 20-word prompts that used to work on DALL·E 3 produce mediocre results here. Think flat lighting, generic compositions, subjects that look like stock photo placeholders. The model has the capability to produce something stunning, but it needs a blueprint, not a sketch.
To unlock GPT Image 2, your prompt needs to be 300+ words with explicit structure: role assignment, subject definition, camera and framing, lighting, palette, layout logic, constraints, and output spec. This is not about stuffing in random adjectives. Each block does a specific job. Role assignment tells the model what kind of creative it is acting as: commercial photographer, concept artist, editorial illustrator. Subject definition pins down every visual attribute of your main element before the model starts guessing. Camera and framing tells it whether you want a wide environmental shot or a tight portrait crop. Lighting specifies temperature, direction, and mood. Palette anchors the color story. Layout logic defines how negative space, hierarchy, and balance should work. Constraints rule out what you do not want. Output spec locks in format, aspect ratio, and resolution intent.
Miss two or three of those blocks and the model fills in the gaps however it wants. Sometimes that works. More often it does not. The model rewards serious prompt engineering more than anything else out there right now.
u/peakpirate007 built Depikt to stop rebuilding that scaffolding from scratch every single time.
The unexpected part
Most prompt tools give you a library and call it a day. Depikt has three parts working together. A library of 290 production-grade prompts with sample outputs. A generator that turns a rough idea into a fully structured prompt. And a critique tool that takes any prompt you paste in and flags the weak blocks.
Missing constraints? Vague subject? No output spec? It flags each one with a reason. That last part is what makes this different from just collecting good prompts. Other tools optimize for discovery. Depikt optimizes for understanding. There is a real difference between having access to a great prompt and knowing why it works. The critique loop closes that gap. You are not just getting a better output on this particular image. You are building a mental model for why structured prompts outperform lazy ones every single time.
The 290-prompt library is also more curated than it sounds. Each entry comes with a rendered sample output so you can see the actual result before you use the prompt. That matters because prompt quality is hard to judge in isolation. Seeing the output alongside the structure is how patterns start to click.
How to use it
- 🔍 Browse the 290-prompt library first. See what a production-grade image prompt actually looks like before you write one. Pay attention to how much specificity goes into a single subject description. Most people underestimate that by a factor of five.
- ✍️ Drop your rough idea into the generator. It outputs the full structured scaffold so you are not guessing at structure. Even if you plan to edit heavily, starting from the scaffold is faster than starting from a blank field. The scaffold forces you to answer questions you would otherwise skip.
- 📋 Paste any existing prompt into the critique tool. It flags every weak block with a reason, not just a score. A score tells you something is wrong. A reason tells you what to fix and in which direction. That specificity is worth a lot when you are iterating fast.
- 🖼️ Every prompt in the library opens directly in Imago (a custom GPT on the OpenAI store) so you can test it immediately without copy-pasting. No switching between tabs, no formatting issues. Point, click, generate.
Pro tip
Do not just copy prompts from the library. Run them through the critique tool and study what makes them pass. After a few rounds, you start writing structured prompts instinctively. The generator becomes a crutch you stop needing.
Take it one step further: pick a prompt that critiques clean, generate an image, then deliberately remove one block at a time and see how the output degrades. That controlled experiment teaches you which blocks matter most for your specific use case. For product mockups, the layout logic block is usually the highest-leverage edit. For atmospheric scenes, lighting almost always makes or breaks the result. For character work, subject definition is where most people leave quality on the table. Run a few before-and-after tests and you will know exactly where your personal prompting gaps are. That is a faster education than any tutorial because the feedback is immediate and visual.
Free at depikt.app. If GPT Image 2 results have been disappointing you, this is probably the missing piece. 🎯
I built a prompt library + generator + critique tool for GPT Image 2
by u/peakpirate007 in PromptEngineering