What we learn building AI-powered web and mobile products for clients: real project numbers, honest tool comparisons, and step-by-step build guides.
SubscribeChoosing how to build an AI feature is one of the first architectural decisions you'll need to make. It affects more than how quickly you can launch.
Understanding how LLM app architecture works means looking at the entire process, not just the model call. The AI product architecture LLM developers build around a model determines what information it receives, how its output is checked, and how the app handles errors.
To integrate ChatGPT into a mobile app reliably, you also need to think about how responses appear on screen, how the AI remembers previous messages, and what happens when a request fails.
A utility crew comes back from the field with hundreds of photos, a stack of technician notes, and a few assets that “looked a little off.” Then what?
"Validated web MVP" and "real mobile app your users can download" are two very different things. And the gap between them is bigger than most founders expect, because it's not a gap you can prompt your way across.
AI can extract the relevant fields, match the same data across documents, flag discrepancies, and log why each result was accepted or escalated.
A new insurance claim can come with a surprisingly messy pile of information. There might be photos of the damage, a policy PDF, a claim form, emails from the customer, and several paragraphs of notes from an adjuster.
A farmer reports hail damage across 400 acres. The insurer now needs to answer a simple question: did the storm actually damage this specific field, and how much?
Open LinkedIn today and you will probably see vibe coding everywhere. Someone built an app in a weekend. Someone else says they replaced an entire development team with a few prompts.
