When to load the whole knowledge base into context, when to retrieve, and the hybrid prompt that does both — a decision framework with real thresholds.

CAG's famous 40x speedup isn't over RAG — it's over uncached prompting. The real numbers, the long-context evidence, and the honest comparison.

I architect AI systems for a living — and the safety warning rings true. The deploy-what-you-understand principle, from lab scale down to yours.

An Anthropic researcher walked away from the frontier. Why Jacob Coxon's warning about the AI race deserves attention — from someone who builds AI.

Most automation projects fail by automating everything. The 80/20 rule: automate the predictable, design the exceptions, keep humans where they matter.

AI implementations are won or lost before anything gets built. The two-week current state assessment: silos, interviews, pain math, and the blueprint.

A smaller model won't give the identical answer a frontier model would — but for most business work both answers are acceptable, and one costs a fraction. Here's the engineering that makes that true, with the research behind it.

Everyone asks which AI tool to buy. Almost nobody asks whether their information is organised enough to use one. Here's why organising is engineering work — and the four rules I apply before any AI touches a knowledge base.

They said prompt engineering would be dead by now. Instead it became the core of context engineering, and both live inside orchestration. Here are the three nested layers, and how to tell which one your agent is failing at.

Gartner: 60% of AI projects without AI-ready data get abandoned. Why the data foundation — not the model — decides whether your AI enablement survives.