Engineering Practice
AI Integration Approaches
Integrate AI where it improves workflows, not where it adds novelty.
Pragmatic AI integration focused on human-in-the-loop systems, domain guardrails, and measurable operational impact.
Foundations
Core Principles
- Prioritize high-friction workflow steps where AI can reduce cycle time.
- Separate model behavior from business-critical rule enforcement.
- Track evaluation quality with task-specific acceptance metrics.
Execution
Implementation Patterns
- Retrieval-augmented workflows backed by curated business context.
- Confidence-based escalation paths for analyst review.
- Prompt and model versioning integrated with release governance.
Production
Delivery Methodology
- Operational monitoring for drift, latency, and failure modes.
- Clear fallback behavior to deterministic workflows when needed.
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