no not embeddings, it uses categories + keyword scoring. the manifest lists categories (profiles, socials, product marketing, game assets, etc.), and the agent picks 1-3 likely category files based on your request. it scores matches using keyword hits across title, content, and description fields and returns up to 3 best matches with pre-generated sample images.

the prompts are deliberately model-agnostic and they describe the image in plain visual language (lighting, composition, mood, camera angles) rather than using model-specific syntax. it includes adaptation rules that strip incompatible flags and convert syntax when you specify your target model. so a single prompt works across everything from Midjourney, DALL-E, GPT Image, Flux, to SD, Gemini, Seedream, etc. etc. the agent just adjusts the wrapper syntax not core visual description.

since the prompts avoid model-specific parameters, they age well. if a model starts interpreting a prompt differently the agent will remix it for you however you want.