Prompt engineering is not a substitute for system design
Prompt engineering is a real skill. It is also the most misused tool in AI development: applied to problems that require system redesign, not better prompts.
Evaluating LLMs for production: what benchmarks don't tell you
Public benchmarks measure models under controlled conditions. How to build an evaluation on your data and quality criteria that predicts production outcomes.
The hidden cost of context switching in AI workflows
Multi-step AI workflows lose information at every handoff, where accuracy degrades, latency compounds, and cost accumulates. Most teams do not measure it.
Why AI systems drift without contracts
AI systems rarely fail loudly. They drift because the assumptions behind inputs, outputs, and behavior are never made explicit enough to enforce.
Per-tenant AI cost attribution: why aggregate dashboards are not enough
Aggregate AI spend hides who is driving cost. Per-tenant attribution shows who to charge, who is profitable, and where margins leak.
Why your AI proof of concept works but your product doesn't
AI proofs of concept work under curated conditions: controlled inputs, invisible costs, no latency limits. Production removes every one of them.