How should I write prompts for Vision-Language Models, and how do I measure their accuracy?

For VLMs like OWL-ViT, LongCLIP, or LLaVA OneVision, prompts should be explicit, structured, and task-specific (e.g., “Describe the objects in this image,” “Locate all emergency vehicles,” “Answer the question based only on the image”). Accuracy can be evaluated using standardized benchmarks (VQA, COCO retrieval, phrase grounding, open-vocabulary detection) or domain-specific metrics such as answer correctness, grounding precision, or retrieval recall. The SDK includes evaluation utilities to run these tests locally on the silicon for consistent measurement.