For creating specific image editing capabilities with AI, a small, curated dataset of "before and after" examples yields better results than a massive, generalized collection. This strategy prioritizes data quality and relevance over sheer volume, leading to more effective model fine-tuning for niche tasks.

The handoff between AI generation and manual refinement is a major friction point. Tools like Subframe solve this by allowing users to seamlessly switch between an 'Ask AI' mode for generative tasks and a 'Design' mode for manual, Figma-like adjustments on the same canvas.

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.

Midjourney's mood board feature can average out the aesthetics of multiple images, leading to generic results. For more precise control, use individual images as style references (`s-refs`). This allows the model to pull more distinct and impactful stylistic elements.

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.

The FLUX Kontext model for JPEG artifact removal isn't a simple automated filter. It leverages text prompts to guide the restoration process, allowing users to describe the image's original content to help the AI more accurately reconstruct details lost to compression.

Don't accept the false choice between AI generation and professional editing tools. The best workflows integrate both, allowing for high-level generation and fine-grained manual adjustments without giving up critical creative control.

AI is incredibly fast for generating the initial version of a feature. However, for small, precise changes like altering a color or text, using a direct visual editor is much faster and more efficient than prompting the AI again. An effective workflow blends both approaches.

Instead of describing UI changes with text alone, Google's AI Studio allows users to annotate a screenshot—drawing boxes and adding comments—to create a powerful multimodal prompt. The AI understands the combined visual and textual context to execute precise changes.