Avoid writing long, paragraph-style prompts from the start as they are difficult to troubleshoot. Instead, begin with a condensed, 'boiled down' prompt containing only core elements. This establishes a working baseline, making it easier to iterate and add details incrementally.
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With models like Gemini 3, the key skill is shifting from crafting hyper-specific, constrained prompts to making ambitious, multi-faceted requests. Users trained on older models tend to pare down their asks, but the latest AIs are 'pent up with creative capability' and yield better results from bigger challenges.
Once you've identified the core components of an image, structure them into a repeatable formula. This template allows anyone on your team, even non-designers, to generate consistent, on-brand assets by simply filling in the blanks, effectively turning prompting into a scalable system.
Before delegating a complex task, use a simple prompt to have a context-aware system generate a more detailed and effective prompt. This "prompt-for-a-prompt" workflow adds necessary detail and structure, significantly improving the agent's success rate and saving rework.
Instead of spending time trying to craft the perfect prompt from scratch, provide a basic one and then ask the AI a simple follow-up: "What do you need from me to improve this prompt?" The AI will then list the specific context and details it requires, turning prompt engineering into a simple Q&A session.
When using "vibe-coding" tools, feed changes one at a time, such as typography, then a header image, then a specific feature. A single, long list of desired changes can confuse the AI and lead to poor results. This step-by-step process of iteration and refinement yields a better final product.
Instead of random prompting, break down any desired photo into its fundamental components like shot type, lighting, camera, and lens. Controlling these variables gives you precise, repeatable results and makes iteration faster, as you know exactly which element to adjust.
Achieve higher-quality results by using an AI to first generate an outline or plan. Then, refine that plan with follow-up prompts before asking for the final execution. This course-corrects early and avoids wasted time on flawed one-shot outputs, ultimately saving time.
To get superior results from image generators like Midjourney, structure prompts around three core elements: the subject (what it is), the setting (where it is, including lighting), and the style. Defining style with technical photographic terms yields better outcomes than using simple adjectives.
Getting a useful result from AI is a dialogue, not a single command. An initial prompt often yields an unusable output. Success requires analyzing the failure and providing a more specific, refined prompt, much like giving an employee clearer instructions to get the desired outcome.
When a prompt yields poor results, use a meta-prompting technique. Feed the failing prompt back to the AI, describe the incorrect output, specify the desired outcome, and explicitly grant it permission to rewrite, add, or delete. The AI will then debug and improve its own instructions.
