Field notes from shipping production AI — what works, what breaks, and what we'd do differently. Practical writing from the senior engineers doing the work, on getting AI out of the pilot stage and into systems you can trust.
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.
