AI Features with LLMs: Retrieval, Evaluation & Guardrails

LLM-powered features that earn their place: grounded in your data through retrieval, measured against evaluation sets, kept within a cost budget and fenced by guardrails.

Many AI features fail for ordinary engineering reasons: nobody measured whether the answers were right, costs grew with usage, or the model was trusted with decisions it shouldn’t make. I treat an LLM like any other unreliable dependency, with contracts, tests, limits and fallbacks. Start with the task. Summarizing a ticket thread, drafting a reply, extracting fields from a document and answering questions from a knowledge base are different problems with different failure modes. I pick the narrowest one that delivers value and define what a good output looks like before writing a prompt…