Field notes & essays
In-depth, honest articles on custom software, CRM automation, HubSpot, integrations, and the judgement calls in between — written from real work, not summaries.
There's enormous pressure to 'add AI' to everything right now. Some of it is genuinely transformative. A lot of it is an expensive way to do worse what a simple rule already did well. Here's how to tell them apart.
Most teams ship AI features on gut feel — it looked good in a few tests, so it went live. Then a prompt tweak silently breaks something and nobody notices. Evaluation is what turns AI from a gamble into engineering.
Everyone can wire an LLM to a vector database in an afternoon. The gap between that demo and a system people trust is almost entirely in retrieval — and retrieval is where most RAG projects quietly fail.
3 articles in AI Engineering