Prompt engineering is not a substitute for system design
Prompt engineering is a real skill with real leverage. It is also the most commonly 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 what models can do under controlled conditions. Production performance depends on how models behave on your data, in your context, against your quality criteria. Here is how to build an evaluation that actually predicts production outcomes.
The hidden cost of context switching in AI workflows
Multi-step AI workflows lose information at every boundary. The handoff between steps is where accuracy degrades, latency compounds, and cost accumulates. Most teams do not measure it.
Why AI systems drift without contracts
AI systems degrade silently over time. Not because the model changes, but because the assumptions baked into the system (about 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 tells you what you're paying. Per-tenant attribution tells you who's driving it, what you can charge for it, and which tenants are profitable. Building it is harder than it looks.
Why your AI proof of concept works but your product doesn't
AI proofs of concept are optimized to demonstrate capability under conditions that don't hold in production. Here is what changes when the demo environment goes away.