Checkpoint
One last thing before we move on. Same surface as a write step — but the lesson doesn't complete until this passes.
Final drill. You're auditing a real image pipeline at an agency.
Write audit_image_pipeline(tasks) that takes a list of task
dicts (same shape as the previous step) and returns a list of
dicts, one per task, in the same order:
{"task_id": ..., "model": ..., "specialist_used": bool}
model comes from your pick_image_model(task) routing.
specialist_used is True if the chosen model is one of the
three specialty picks: ideogram-3, flux-kontext-pro, or
flux-1.1-pro. Everything else is False (including the budget
fallback gpt-image-1-mini).
Each task carries a task_id field (string). Preserve it in
the output.
Five tasks run. Expected output:
T1: ideogram-3 specialist=True
T2: flux-kontext-pro specialist=True
T3: nano-banana specialist=False
T4: midjourney-v7 specialist=False
T5: gpt-image-1-mini specialist=False