Image generation control
The practice of using specific inputs—ranging from natural language to structured data—to manipulate AI Image Generators for precise, predictable, and repeatable visual outputs.
Control Modalities
- prompt-engineering: Utilizing natural language descriptions to influence subject, lighting, and composition.
- Structured Prompting: Implementing formal data formats to define scene parameters and reduce ambiguity.
- JSON Prompts: A technique for ChatGPT Image 2.0 that uses json objects to achieve granular, advanced control over the generation process.
- Allows for a highly precise workflow by establishing structured instructions for the model to interpret.
- Parameter Tuning: Manipulating technical variables (e.g., aspect ratio, seeds, or weights) to refine iterative outputs.
Related Concepts
- generative-ai
- prompt-engineering
- Automation Workflows
Sources
Source Notes
- 2026-04-10: New Nano Banana Update: How to Use Nano Banana 2
- 2026-04-26: Craig Does AI: JSON Prompts for Advanced ChatGPT Image 2.0 Control · ▶ source
- 2026-04-07: Analysis of Leading AI Models Capabilities Pricing Tiers and Optimal · ▶ source
- 2026-04-17: OpenAI Codex Becomes Unified AI Everything App for Software Developmen · ▶ source