1. Build a prompt evaluation task app
Define a task app that evaluates prompt performance on your task (classification accuracy, QA correctness, etc.).→ Read: Task App requirements
2. Deploy and verify the service
Smoke-test locally, then deploy to Modal or your host of choice once health checks pass.→ Read: Deploying task apps
3. Author the prompt learning config
Capture algorithm choice (GEPA or MIPROv2), initial prompt template, training/validation seeds, and optimization parameters in TOML.→ Read: Prompt learning configs
4. Launch the optimization job
Runuvx synth-ai train --config config.toml to create the job and stream status/metrics.→ Read: Launch training jobs
5. Query and evaluate results
Use the Python API or REST endpoints to retrieve optimized prompts and evaluate them on held-out validation sets.→ Read: Querying results