Built a highly responsive web application prototype focusing on smooth user interfaces and swift database connectivity. Optimized web performance to ensure rapid load times and cross-platform compatibility.

I built an AI support-ticket router on Replit, then tested it against the real model. Three things broke that no mock would have caught.

A silent failure. The first run sent 33 of 40 tickets to "needs a person". The newer Claude model rejects the temperature setting, and my code swallowed the error, so every call quietly failed. Now every failed call is logged.

The model thinks before it answers. With a tight token limit, some tickets got no answer at all and some draft replies stopped mid-sentence. Both have room now, and a cut-off draft is never saved.

One incident looked like four bugs. Four Safari checkout failures each got a different issue key, so the "3+ tickets, same problem" alert never fired. Sorting tickets in arrival order and asking for keys that name the problem rather than the customer turned them into one flagged incident.

If you're adding an LLM feature to your product, test it on real inputs early. The failures are rarely where you expect.

Live demo: https://sorta--krishnanbhupath.replit.app

the swallowed temperature error one is brutal