5 posts
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 ...
Text-only systems break as soon as the evidence stops being mostly text, which is exactly what happens in accessible travel planning when photos, floor plans, route maps, captions, and measurements all shape the answer.
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.
Basic retrieval-augmented generation, or RAG, is still the default grounding pattern for most production systems. If you have a large corpus, frequent updates, and a need to show where an answer came from, retrieval remains the cleanest starting point. But there is a practical limit case where si...
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...