Patterns & Practices

These patterns describe how I approach delivery, system design, and decision-making across different contexts. They are not tied to a single role or employer, but reflect repeatable practices I apply when working in complex or constrained environments.

Controlled Deployments

I treat deployments as a risk management problem, not just a delivery milestone. In constrained or regulated environments, predictable change matters more than raw speed.

Without controlled deployment patterns, teams tend to rely on heroics, manual intervention, or implicit knowledge during failure. This increases operational risk and erodes trust.

• Repeatable pipelines with explicit gates and rollback paths
• Infrastructure and configuration reviewed as code
• Changes scoped to reduce blast radius and surprise

Appears in: Cloud Infrastructure & CI/CD Foundations

Cost-Aware Design

I treat cost as an architectural constraint, not a cleanup task. Decisions about scope, runtime behavior, and observability shape cost long before billing alerts fire.

Cost-aware design focuses on visibility and guardrails rather than aggressive optimization. The goal is predictable decision-making, not chasing savings at the expense of reliability or ethics.

• Early visibility through dashboards and alerts
• Right-sizing and lifecycle policies to prevent waste
• Explicit tradeoffs between cost, capability, and risk

Appears in: OtakuAI Case Study

Evidence-Bound AI Systems

I design AI systems to operate within explicit knowledge boundaries. When information is uncertain, incomplete, or unavailable, the system should say so clearly rather than inventing answers.

This pattern prioritizes trust over coverage. Refusal, redirection, and transparency are treated as first-class behaviors, not failure modes.

• Curated knowledge sources and retrieval constraints
• Explicit refusal and redirect behaviors
• Clear separation between fact, inference, and speculation

Appears in: GenAI Portfolio Assistant