Retrieval API contract
Ingestion, access-aware retrieval, grounded answers, feedback, health checks, trace IDs, citations, and explicit abstention.
Sanitized contracts, checklists, and operating templates that show how I think about APIs, AI quality, incidents, communication, and professional proof.
These are reference artifacts rather than employer documents. They preserve useful engineering structure without revealing internal systems or data.
Ingestion, access-aware retrieval, grounded answers, feedback, health checks, trace IDs, citations, and explicit abstention.
A release-oriented checklist spanning datasets, ACLs, ingestion, retrieval, groundedness, reliability, cost, feedback, and rollback.
A blameless template connecting impact, detection, timeline, root cause, AI-specific checks, corrective actions, and verification.
The materials below prepare the introduction video and attributed recommendations without manufacturing either.
A complete 60-second script, shot plan, and recording checklist ready for an authentic camera or voice recording.
Open script ↗A concise outreach message, useful prompts, approval record, and publication format for real, attributable recommendations.
Open request kit ↗A working browser-side retrieval lab with visible rankings, thresholds, citations, and evaluation rather than a static screenshot.
Launch lab ↗Instrumented portfolio events, provider adapters, recommended funnel questions, and a privacy checklist before connecting an account.
Read setup ↗{
"status": "answered",
"answer": "Evidence-grounded response",
"citations": [
{
"documentId": "public-safe-id",
"score": 0.87
}
],
"traceId": "request-trace"
}
Make the behavior observable enough that another engineer can challenge it.
Explore the case study for the tradeoffs, the lab for the behavior, or the résumé library for the concise professional view.