Teams approve LLM projects based on per-query cost estimates, then get blindsided by the actual invoice. The gap between estimate and reality is not a rounding error. It is a structural problem: the e

Teams new to applied AI often fixate on which foundation model to use. The more important decision is how to shape the model's behavior for your specific task. The three primary levers are prompt engi

You manage a software team. You do not assign every task. You do not review every decision before it is made. You set the objectives, define the constraints, and trust the team to plan its own sprint,

Choosing an embedding model is one of the first decisions you make when building a retrieval-augmented generation system, and it is one of the hardest to reverse. The model you pick determines your ve

Every few months, someone declares that a new tool has made data engineering obsolete. First it was managed warehouses. Then dbt. Then generative AI that writes SQL. Each time, the prediction is the s

Most data catalog projects die within six months. The tool gets purchased, a team populates it with metadata for a few hundred tables, enthusiasm fades, and twelve months later the catalog is a stale

Predictions about technology roles are usually wrong in predictable ways. They overestimate the speed of change, underestimate the persistence of legacy systems, and assume that technical trends will

The decision to centralize AI infrastructure into a platform team usually comes after a period of decentralized pain. Three product teams independently built model serving pipelines. None of them shar

The framing of AI systems as either "copilots" (human-in-the-loop, AI assists) or "autopilots" (human-out-of-the-loop, AI acts independently) has dominated the conversation about AI autonomy for two y