5 posts
An AI system can return a polished answer while failing at retrieval, using stale evidence, calling the wrong tool, or changing the wrong record. If the team evaluates only the final prose, those failures collapse into one vague score. If the team logs everything without a measurement plan, it ge...
By the time a team reaches an advanced AI travel copilot, the hard question is no longer "Which model should we use?" It is "What has to happen, in what order, with what evidence, with what state, and under whose approval before this system can be trusted in production?" That is an architectural ...
Most teams first meet document processing through OCR. The problem seems straightforward: convert pages into text, index the text, and let retrieval or an LLM answer questions from it.
Large language models are useful because they can synthesize, explain, and transform information in fluent language. They are unreliable when we ask them to know something current, something private, or something that needs verifiable support. A model may have seen similar material during trainin...
Customer support is a useful capstone because one message can require issue decomposition, private records, policy evidence, tools, state, authorization, and human handoff at the same time.
