Text-to-Image Generation: Prompt Engineering

Text-to-image AI models interpret natural language prompts to generate corresponding images. These models use transformer-based architectures, such as CLIP (Contrastive Language-Image Pretraining), to understand textual descriptions and map them to visual representations.

Prompt engineering plays a critical role in AI image generation. Users can refine outputs by using structured keywords, weight adjustments, and context-based modifiers. For example, adding stylistic terms like "hyper-realistic," "oil painting," or "sci-fi concept art" influences the generated image’s style and mood.

Negative prompting is another technique that allows users to exclude undesired elements from AI-generated images. This helps improve composition accuracy and artistic intent.