Describe an AI-powered workflow you took from an ambiguous business problem to production.I begin by interviewing users and defining the operational decision, failure modes, success metrics, and boundaries for automation. I then build a small vertical slice with structured inputs and outputs, add evaluation cases and human review where needed, and monitor production quality before expanding the workflow.
How would you build an AI agent for insurance benefits verification while protecting PHI?I would use least-privilege service access, encrypt data in transit and at rest, avoid PHI in logs and prompts where possible, and maintain auditable records of access and changes. The workflow would use typed tool interfaces, validation rules, retries, confidence thresholds, and escalation to an operator for uncertain or high-risk cases.
What does a strong AI coding-agent setup for a large codebase look like?It includes clear repository instructions, scoped skills, tool permissions, MCP integrations, test and lint hooks, architecture documentation, and task-specific subagents. I run agents on well-defined tasks, require them to produce verifiable changes, and review outputs through tests, diffs, security checks, and product acceptance criteria.
How do you evaluate an LLM feature before and after launch?Before launch, I create a representative dataset with expected outcomes, edge cases, safety cases, and measurable grading criteria. After launch, I track quality, latency, cost, tool failures, escalation rates, and user feedback, then use production samples to continuously improve the evaluation suite.
How do you approach a feature that requires frontend, backend, data model, and infrastructure changes?I first define the user outcome and system contract, then break the work into API, schema, interface, workflow, and deployment concerns. I deliver a narrow end-to-end version early, use migrations and feature flags for safe rollout, and retain ownership through monitoring, incident response, and iterative improvement.
