Principles, Core Strategies, and Improvement Checklist for Effective System Prompt Design in Image Generation Agents
From Prompt to Perfection: The Art of the Image Generation Agent.
An image generation AI agent does much more than simply “convert text to image.” To succeed, it must achieve creativity, accuracy, and user satisfaction simultaneously. For this, system prompt design must be strategically crafted and continuously refined.
Below, I present the core principles, strategies, and an improvement checklist to help you build such an advanced system.
1. Clear Purpose Definition and Scope Setting
Core Idea:
Define the agent’s generation purpose and clearly set the scope of user expectations.
✅ Real-World Example:
A startup developing an AI for product package design initially tried to cover “all styles,” which confused users. After narrowing the system prompts to focus on “simple, modern, brand-friendly” outputs, user satisfaction significantly improved.
✔ Improvement Checklist
Have you clearly defined the main purpose and key styles your agent will cover?
Have you documented both what the agent will and will not provide?
Have you recorded past cases where unclear scope caused issues?
2. Multi-Layer Instruction Structure Design
Core Idea:
Break down prompts into layered components—topic, style, color, emotion, details—so complex requests can be handled with clarity.
✅ Strategic Approach:
[Topic]→[Style]→[Color/Tone]→[Detailed Description]→[Output Format]
This structure helps decompose user input and integrate it into prompts with stronger control and predictability.
✅ Real-World Example:
By transforming prompts from simply “summer beach” to “summer beach, watercolor style, blue tones, warm atmosphere, high-resolution poster output,” the system improved consistency by over 40%.
✔ Improvement Checklist
Have you structured prompt input components (topic, style, color, emotion, output) hierarchically?
Have you distinguished between required and optional elements in each layer?
Do you have a system to detect and notify users about conflicting inputs?
3. Data-Driven Failure Pattern Analysis
Core Idea:
Analyze failed prompt cases to identify and correct recurring issues.
✅ Real-World Example:
When the AI repeatedly struggled with contradictory prompts like “classic, futuristic,” the system was enhanced to detect such conflicts and prompt the user to adjust.
✔ Improvement Checklist
Are you regularly collecting data on failed and successful prompt cases?
Have you categorized and tagged recurring failure types?
Have you developed improvement strategies (A/B testing, wording adjustments, user guidelines) for each failure type?
4. Optimization Aligned with AI Model Characteristics
Core Idea:
Understand the strengths, limitations, token limits, and interpretation nuances of each AI model, then optimize prompts accordingly.
✅ Real-World Example:
For MidJourney, adding emotional keywords (e.g., dreamy, vivid) worked best, while DALL·E benefited from more explicit object specifications (e.g., type of chair, lighting), improving results for both.
✔ Improvement Checklist
Have you documented token limits, strengths, and weaknesses for each AI model you use?
Have you developed optimized prompt templates for each model?
Are you tracking and recording quality differences between models on identical inputs?
5. Integration of User Feedback
Core Idea:
Continuously collect and analyze real-time user feedback to improve system prompt design.
✅ Real-World Example:
When feedback repeatedly flagged outputs as “too abstract,” the system adjusted the prompt recommendation engine to only suggest abstract descriptors (like ‘dreamlike,’ ‘surreal’) when user approval rates were high.
✔ Improvement Checklist
Are you automatically collecting user feedback (satisfaction scores, selection patterns)?
Are you analyzing feedback data to identify improvement directions?
Have you built an improvement roadmap for each negative feedback type?
6. Safe Experimentation Space for Creativity
Core Idea:
Provide a safe, user-friendly experimentation environment where users can freely test various styles and approaches.
✅ Real-World Example:
Introducing an “experimental mode” with prebuilt prompts allowed new users to try multiple options with just a few clicks, shortening their learning curve and boosting success rates.
✔ Improvement Checklist
Are you providing a library of sample prompts and examples?
Have you implemented a user-defined prompt simulation feature?
Have you created a credit-based system to let users experiment without penalty?
Final Conclusion
An effective image generation agent is never built by accident.
Meticulously designed system prompt design is the key to maximizing an AI’s potential and elevating user satisfaction.
When you strategically design every step—from purpose definition and multi-layer structuring, to data analysis, model-specific optimization, feedback integration, and safe experimentation—and regularly check the improvement checklist, your agent can truly deliver outstanding performance.
With this framework, your image generation system can evolve to the next level.