Conclusion

Image models are shifting from “cool demos” to production tools that generate real artifacts—screens, flyers, product visuals, and brand edits that influence decisions and ship to customers. The core lesson of this cookbook is simple: you can’t evaluate these systems with generic “looks good” scores. You need workflow-specific evals that are repeatable across model versions, prompts, and settings. Multimodal LLMs make this practical by acting as scalable judges—when paired with tight rubrics, structured outputs, and human calibration.

A practical vision-eval program starts small and gets sharper over time:

• Start with gates. Add strict pass/fail graders for the failure modes that break real work: missing required components, incorrect copy, edits spilling outside the intended region, or unintended changes to preserved areas. This prevents “pretty but wrong” outputs from masking regressions.
• Layer in graded metrics. Once hard failures are controlled, use 0–5 rubrics to capture what matters for usability and quality in each workflow (e.g., hierarchy in UI mockups, brand fit in marketing, or fidelity/preservation in editing).
• Tag failures to iterate faster. Consistent failure tags turn a pile of outputs into actionable engineering work: you can quantify what’s breaking, find clustered root causes, and track progress as you tune prompts, model settings, masks, or post-processing.
• Use humans strategically. Humans add the most value on subjective or ambiguous dimensions (“vibe,” usability clarity, trustworthiness), but only if you keep rubrics tight and use calibration anchors to prevent drift.
• Treat the harness as a product. The reusable harness you built—test cases, runners, graders, and stored artifacts—creates the foundation for regression testing, parameter sweeps, and side-by-side comparisons. Over time, your eval suite becomes your safety net: it catches subtle failures early and makes improvements measurable.
• Evolve as needed. You can redesign your grader prompts with the complex metrics over time. One natural next step would be splitting the single-shot grader prompt into multiple calls to focus on a specific metric.

Build evals that reflect how images are actually used, enforce correctness before aesthetics, and make iteration data-driven. When your evals are aligned with real workflow requirements, image generation and editing stop being unpredictable art projects and become tools teams can trust.