GPT-5 Prompt Template — 6-Band Structure for OpenAI GPT-5

Why GPT-5 Needs Structured Prompts

Compare model responses on representative tasks with explicit instructions and factual checks. Model capability labels do not establish how either model handles a vague prompt.

Use the sinc-LLM six-field template to state GPT-5’s task, evidence, constraints and output format. Review the response for factual support and requirement compliance.

GPT-5 Prompt Template

{
  "formula": "x(t) = \u03a3 x(nT) \u00b7 sinc((t - nT) / T)",
  "T": "specification-axis",
  "fragments": [
    {"n": 0, "t": "PERSONA", "x": "Senior software architect with 15 years of distributed systems experience. Expert in AWS, Kubernetes, and event-driven architecture."},
    {"n": 1, "t": "CONTEXT", "x": "Migrating a monolithic Django application to microservices. Current system handles 10K RPM. Target: 100K RPM with 99.99% uptime. Budget: $50K/month AWS spend."},
    {"n": 2, "t": "DATA", "x": "Current stack: Django 4.2, PostgreSQL 15, Redis 7, Celery. 47 Django apps, 312 models, 1.2M lines of Python. Top 5 bottleneck endpoints identified via APM."},
    {"n": 3, "t": "CONSTRAINTS", "x": "Must use strangler fig pattern for incremental migration. No big-bang rewrite. Each microservice must be independently deployable. Must maintain backward compatibility with existing API consumers during migration. Database per service with eventual consistency via events. All inter-service communication via async message queue (SQS or Kafka). Migration must complete within 6 months. Each phase must be production-safe with rollback capability. No service may exceed 5K lines of code. Must include comprehensive monitoring and alerting from day one."},
    {"n": 4, "t": "FORMAT", "x": "Markdown document with: (1) Migration phases timeline, (2) Service boundary diagram in Mermaid, (3) Data migration strategy per service, (4) Risk matrix, (5) Monitoring checklist."},
    {"n": 5, "t": "TASK", "x": "Design the complete microservices migration plan for this Django monolith, starting with the 5 bottleneck endpoints as the first extraction candidates."}
  ]
}

GPT-5 Specific Optimizations

Check the exact model’s current capabilities before using these template features:

• DATA: supply relevant reference material within the selected model’s verified context limits.
• CONSTRAINTS: state conditional requirements explicitly and test whether the response follows them.
• DATA: confirm support for the intended input type before providing images or documents.
• FORMAT: state the output schema and validate the returned structure and contents. A requested format does not guarantee exact compliance.

GPT-5 vs GPT-4o: When to Upgrade

See the full LLM cost comparison for detailed pricing across all models.

More Model-Specific Templates

Use the right template for each model: ChatGPT, Claude, Gemini, Llama, Mistral, DeepSeek, GPT-4, o3, Copilot. Or use the universal sinc-LLM prompt optimizer to generate a structured prompt for any model automatically.