Articles
Analysis, field reports and context on AI in processes, marketing and research.
9 articles
Building a small RAG system: what you actually need
A RAG system sounds like heavy infrastructure. For a small project, four building blocks are enough: documents, an embedding model, a vector database, and a language model. Here's how they fit together conceptually, using Google Gemini and ChromaDB.
Agents in the mid-market: what really happens after the pilot
Almost every company is running an AI pilot by now. Few make it into day-to-day operations. Why that is — and what the exceptions do differently.
Creative production: how much does AI really take over?
Generated creatives have become cheap. That did not reduce the work — it moved it. A look at where the bottleneck sits now.
Understanding context windows: why more is not automatically better
A million tokens sounds like the end of all limits. In practice it is not the size of the window that decides, but what sits inside it.
Evaluation over gut feeling: how to measure the quality of an AI system
“Feels better” is not a metric. How to build a test set that surfaces regressions before customers report them.
A new model generation: what actually reaches everyday work
Benchmarks climb every quarter. For most applications something else has long been decisive: latency, cost and reliability on boring tasks.
RAG in practice: why retrieval matters more than the model
Most disappointing knowledge assistants do not have a model problem. They simply retrieve the wrong documents — which is measurable and fixable.
Attribution in the AI era: measuring what escapes measurement
More and more research ends in an answer instead of a click. What that means for reporting, budget allocation and the question of channel contribution.
AI governance: what teams should document now
Between compliance theatre and ignoring the topic sits a short list of things every team should know anyway. A pragmatic minimum.
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