Citizen developers
Plan a small campus AI project, choose a supported tool, and prepare it for review.
A citizen developer builds a useful workflow or application for a campus need, even when software development is not their primary job. Start with a bounded problem, an owner, and data you are allowed to use.
Choose a starting point
| Need | Start here |
|---|---|
| Chat with documents or use a campus assistant | TritonGPT |
| Run a scheduled or event-driven workflow | n8n |
| Build locally with a coding agent | TritonAI Harness |
| Add a model to your own application | Developer API quickstart |
Use the smallest tool that fits
A repeatable workflow does not always need a custom application. A TritonGPT assistant or an n8n workflow may be easier to operate and support.
Before you build
Write down the outcome, who owns it, and who will use it. Confirm the data classification and the service approved for that data before you upload records or connect a campus system.
Use representative test data while you work out the flow. Do not place API keys, passwords, private records, or access tokens in prompts, screenshots, source files, or repositories.
A practical build path
Define one job
Describe the trigger, the expected result, and the person who checks that result. Keep the first version narrow enough to test by hand.
Choose the service
Use the table above to find the simplest supported path. If you need the Developer API, request access for the specific use case.
Build with safe test data
Test success, empty input, invalid input, timeouts, and partial failures. Add human review before any action that changes a system of record or sends a message on someone else's behalf.
Prepare for other users
Add authentication, clear error messages, logs that avoid sensitive content, an owner, and a way to stop the workflow. Contact tritonai@ucsd.edu before you publish or host a campus-facing application.
Review checklist
- The application has a named owner and a documented purpose.
- The selected Triton AI service is approved for the data in scope.
- Secrets stay in environment variables or an approved secret manager.
- A person reviews consequential output or actions.
- Users can tell when the model is uncertain or the service is unavailable.
- Logs contain enough detail to diagnose a failure without copying private content.
- The project has a support path and a way to disable access.