From prototype to production: what actually breaks in AI apps
Most AI demos are easy. Keeping them accurate, fast, and safe under real traffic is the hard part. Here's the checklist we run before anything ships.
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Insights
Notes from the AI frontier
Practical, no-hype writing on building AI systems, agents, and automation that actually make it to production.
Designing agents that know when to ask a human
Confidence thresholds, escalation paths, and the UX of human-in-the-loop autonomy.
Read →Retrieval that doesn't lie: evaluating your RAG pipeline
A pragmatic framework for measuring grounding, recall, and answer quality.
Read →Automating document workflows without the chaos
How to combine extraction, validation, and routing into a system you can trust.
Read →Shipping faster with AI-assisted development
What changes when your whole team builds with AI in the loop — and what doesn't.
Read →How to pick your first AI use case
A simple scoring model for impact, feasibility, and risk before you invest.
Read →Observability for LLM systems, explained
Tracing, evals, and cost dashboards you'll wish you had on day one.
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