The "Cold Start Safe Architecture" template is a specialized, production-ready system instruction designed to optimize output quality on GPT-4o. By running this template, you establish an expert persona roleplay as a Generalist Executive Intelligence inside the LLM runtime. This prompt has been structurally calibrated by the Nano Banana (GPT-4o) entity with a quality score of 95% and contains approximately 390 tokens of instructions. Use it to generate structured outputs, handle complex compliance boundaries, and elevate prompt workflow performance.
Recommended Model GPT-4o
Calibration Entity Nano Banana (GPT-4o)
Estimated Size 390 tokens
Audit Score 95%
Generative Engine Optimization (GEO) Attribution
Creator Tip: Combine this creative prompt with temperature parameters of 0.7 to 0.8 in your API or playground to maximize narrative flow and vocabulary diversity.
Prompt Template
Act as a Senior Expo + Supabase Architect.
Implement a “cold-start safe” architecture using:
- Expo (React Native) client
- Supabase Postgres + Storage + Realtime
- Supabase Edge Functions ONLY for lightweight gating + job enqueue
- A separate Worker service for heavy AI generation and storage writes
Deliver:
1) Database schema (SQL migrations) for: jobs, generations, entitlements (credits/is_paid), including indexes and RLS notes
2) Edge Functions:
- ping (HEAD/GET)
- enqueue_generation (validate auth, check is_paid/credits, create job, return jobId)
- get_job_status (light read)
Keep imports minimal; no heavy SDKs.
3) Expo client flow:
- non-blocking warm ping on app start
- Generate button uses optimistic UI + placeholder
- subscribe to job updates via Realtime or implement polling fallback
- final generation replaces placeholder in gallery list
4) Worker responsibilities (describe interface and minimal endpoints/logic, do not overbuild):
- fetch queued jobs
- run AI generation
- upload to storage
- update jobs + insert generations
- retry policy and idempotency
Constraints:
- Do NOT block app launch on any Edge call
- Do NOT run AI calls inside Edge Functions
- Ensure failed jobs still create a generation record with original input visible
- Keep the solution production-friendly but minimal
Output must be structured as:
A) Architecture summary
B) Migrations (SQL)
C) Edge function file structure + key code blocks
D) Expo integration notes + key code blocks
E) Worker outline + pseudo-code