1 — Learn how each model thinks There’s no universal prompt. Some models prefer short keywords, others full sentences or structure. Knowing this saves so much trial and error. Kittl helps with prompt bundles made for specific models, so you don’t start from scratch. 2 — Set direction before detail Start with the vibe, not the essay. Mood, style, and visual direction matter more than perfect wording. Once that’s clear, refining is easy. Kittl’s image styles help you lock the look first. 3 — Keep

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High-capacity vision-language models (VLMs) are trained on large web datasets, enabling them to effectively recognize visual and language patterns and function across multiple languages. However, for robots to reach a similar level of proficiency, they would need to gather firsthand data across various objects, environments, tasks, and situations. In this context, researchers have introduced Robotic Transformer 2 (RT-2), a vision-language-action (VLA) model that learns from both web and robotics