Verified against the current implementation
01
Prerequisites
Before implementation, identify the problem owner, available data, and a measurable success criterion. A small representative sample of real data is more useful than a large synthetic dataset.
- A focused use case with defined inputs and outputs
- A measurable quality, latency, or cost target
- Representative data that exposes real constraints and edge cases
02
Choose the right technical path
Let the problem shape the architecture: RAG for grounded knowledge, agents for multi-step workflows, computer vision for visual input, and MLOps for the model lifecycle.
If the problem does not map cleanly to one architecture, run a short discovery first and avoid building several capabilities at once.
03
Check service health
The health endpoint reports application and database availability. It does not require authentication and works well as a readiness check.
bashTusi
curl --request GET \
--url https://tusihub.ir/api/health \
--header 'Accept: application/json'04
Production checklist
AI quality is not only a model concern. Test failure paths, observability, and system behavior on invalid data.
- Validate input and enforce payload limits
- Track quality, latency, and errors without storing sensitive data
- Define timeouts, bounded retries, and predictable fallbacks
- Document the runbook and rollback path before release
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