We put an AI agent on our support channels about eight months ago. First three months were rough, but not for the reason we expected. Every time the bot gave a bad answer, our team assumed something was wrong with the model. Almost every time, it was a gap in what we’d actually fed it. The thing that moved the needle was dead simple. Once a week, someone on our team pulls up the low-confidence responses in Chatbase’s logs, finds where the bot didn’t have a good answer, and either adds a Q&A pair or tightens the source doc. No retraining, no model swaps, just treating it like any other system that needs regular input to stay sharp. The confidence score on each response ended up being the single most useful thing in the entire setup. Low confidence almost always pointed to a gap in our knowledge base, not a limitation of the AI itself. Most teams I’ve talked to who are struggling with this set the thing up, got excited for a week, then stopped feeding it. The ones getting good results are the ones running it like an ops process. How is everyone else handling the ongoing maintenance side? Do you have a formal review cadence or is it still whoever-has-time-this-week? submitted by /u/Many-Personality-157
Originally posted by u/Many-Personality-157 on r/ArtificialInteligence