A practical workflow for prototyping LLM applications in Google AI Studio using a support ticket triage example. Covers selecting Gemini models, writing system instructions, enabling structured output, building test sets with at least 20 examples, saving prompt versions, exporting starter code, adding regression tests, and logging production behavior. Also highlights common mistakes like treating AI Studio results as production-ready, skipping regression tests, overfitting to single examples, and ignoring safety settings. Includes a checklist for moving a prototype into a real application.
Table of contents
How to Prototype LLM Apps in Google AI StudioWhat Google AI Studio Is Good ForExample Prototype: Support Ticket TriageStep 1: Open Google AI Studio and Create a New PromptStep 2: Select the Right Gemini ModelStep 3: Write the First System InstructionStep 4: Add a Realistic Test InputStep 5: Turn On Structured Output When AvailableStep 6: Test More Than One ExampleStep 7: Adjust Safety Settings CarefullyStep 8: Save Prompt Versions Outside the PrototypeStep 9: Export Starter Code and Move Into Your AppStep 10: Add Regression Tests Before ShippingStep 11: Log Real App Behavior After DeploymentCommon Mistakes to AvoidA Practical Prototype ChecklistWhen to Leave AI Studio2 Impressions