GenAI Engineering
Building with generative AI in production — the engineering discipline behind the hype. These posts explore the real work of shipping AI systems: architecture trade-offs, failure modes, cost modeling, and the gap between weekend demos and production-grade software.
You're Not Adopting GenAI. You're Coping With It.
Pragmatic GenAI insights on probabilistic systems deployed with deterministic playbooks — why organizations cope instead of adopt.
Lines of Code Are Dead. Tokens Aren't the Answer Either.
Metrics theater in GenAI adoption — why token counts and prompt volume measure activity, not outcomes. Stop rewarding consumption, measure what actually matters.
Your GenAI POC Isn't Lying. It's Just Not Telling the Whole Truth.
GenAI POC limitations and demo privilege — pragmatic insights on why weekend prototypes fail in production. Engineering discipline separates demos from systems.
Why pragmat.ai?
Why pragmat.ai believes engineering discipline — not hype — is what separates GenAI prototypes from production-ready systems.