Generative AI (GenAI) is rapidly transforming how instructors and students visualize site-specific environmental proposals—translating data, ecology, and place into images that support design inquiry, critical reflection, and stakeholder dialogue. This 90-minute, hands-on workshop engages participants in the process of using GenAI to explore agricultural and ecological systems rather than simply producing static visual outcomes. Through guided exercises, attendees will use descriptive language derived from real landscapes—such as soils, slope, hydrology, canopy structure, and planting communities—to shape the generative process and strengthen place-based understanding. The session emphasizes iterative thinking: using GenAI to visualize and test environmental concepts like pollinator buffers, riparian restorations, agroforestry windbreaks, and field-to-woods transitions. Participants will practice turning site inventory layers into prompt components, articulating ecological intent (successional stages, structural diversity, and edge conditions), and experimenting with scale, perspective, and seasonality to examine management and stewardship scenarios. Rather than focusing on a fixed toolkit, the workshop centers on process literacy—how observation, language, and iteration form a feedback loop between ecological knowledge and visual representation. Discussion and critique will address ethics, bias, and transparency, including labeling of AI-assisted images, data provenance, and strategies to avoid misleading photorealism. By the end of the session, participants will have experienced the complete generative process from field description to visual interpretation. They will leave with a replicable framework for integrating GenAI into teaching, research, and outreach, using it to foster inquiry, pattern recognition, and visual reasoning within agricultural and environmental disciplines. Designed for faculty and graduate students, the workshop requires no prior AI experience and encourages experimentation with mobile or laptop-based tools using provided site imagery and regional datasets.