GPT-5 is OpenAI's most capable model with a 256K context window, advanced reasoning, and multimodal capabilities. But capability without structure produces noise. This template gives GPT-5 the 6-band specification it needs to deliver maximum signal.
GPT-5 is more capable than GPT-4o — which means it can generate more plausible-sounding nonsense when given vague instructions. Greater model capability amplifies both signal and noise. A raw prompt to GPT-5 gets a more articulate hallucination than the same prompt to GPT-4o.
The sinc-LLM 6-band template channels GPT-5's capability into producing exactly what you specify. No wasted tokens, no hallucinated context, no format guessing.
{
"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 has several characteristics that the sinc-LLM template exploits:
| Criterion | Use GPT-4o | Use GPT-5 |
|---|---|---|
| Task complexity | Routine tasks, simple generation | Multi-step reasoning, complex analysis |
| Context needs | Under 128K tokens | 128K-256K tokens |
| Budget sensitivity | High ($2.50/1M in) | Lower ($5.00/1M in, but fewer retries) |
| Accuracy requirements | Good enough | Must be right first time |
See the full LLM cost comparison for detailed pricing across all models.