Senior engineers writing about building and running production AI.
5 articles
A chatbot responds to a prompt and stops. An AI agent plans a goal, uses tools to carry it out, checks the result, and keeps going until the task is done. The dividing line is autonomy.
RAG makes an LLM answer from your data instead of only its training. Before the model writes, a retrieval step finds the most relevant passages and adds them to the prompt — so the answer is grounded in real, current facts.
Use RAG when the problem is missing knowledge — facts that change or that the model never saw. Use fine-tuning when the problem is behavior — a tone, format, or decision pattern. They solve different problems, and the best systems use both.
Almost every company is running AI pilots. Very few have put one into production. The gap is not the model — it is everything around it.
How a small, senior team using AI agents ships what used to take a team three to four times its size — and keeps it running.
