Hi everyone,
I’m experimenting with Gemini API for a small local service workflow.
The idea is to let customers upload photos of items they want removed, then use Gemini to:
- identify common household items
- group them into categories (furniture, appliances, yard waste, electronics, etc.)
- estimate pickup complexity
- return structured JSON that can be passed to another application for quote generation
For those who have built similar vision workflows:
- Which model has worked best?
- Do you rely on structured JSON output?
- How do you reduce misclassification when multiple items appear in one image?
- Any prompt engineering tips for more consistent results?
I’d appreciate hearing about real-world implementations.