1. Scope + Data Contract
Define user intents, source-of-truth data, and success metrics before any model tuning.
Engineering Process
A Repeatable Flow For Shipping AI Features.
Every project follows the same evaluation-first lifecycle: scope, implement, measure, and only then release.
2. Build Deterministic Interfaces
Implement predictable APIs and guardrails so model behavior is bounded and testable.
3. Evaluate + Observe + Deploy
Validate quality and latency, inspect traces, then deploy with rollback and monitoring.
Release Standards
Quality Gates Before Production
Answer Quality
Faithfulness, relevance, and structured-output validity from curated benchmark datasets.
Operational Health
Latency budgets, failure-rate thresholds, and fallback behavior under degraded conditions.
Regression Safety
No release without automated comparison against previous baseline performance.