Topic

AI engineering, in production

AI engineering is what happens after the demo. It's the work of taking a model that looked impressive in a notebook and turning it into a system that's reliable, observable, and economically sustainable in production. Our coverage focuses on the parts of AI that vendors gloss over: retrieval pipelines that don't hallucinate, evaluation harnesses that catch regressions before users do, prompt and tool-use patterns that survive model updates, and the boring-but-critical engineering around cost, latency, and safety. We write about real systems we've shipped for clients across Australia, the UAE, and Qatar — what worked, what we'd build differently, and what the hype cycle gets wrong.

• AI engineering
• LLM applications
• production AI
• AI evaluation
• retrieval augmented generation