I keep seeing the same problem with AI projects. The knowledge base looks fine. The docs are there. The RAG pipeline technically works. But the AI still forgets rules, pulls the wrong context, gives inconsistent answers, or somehow burns through a ridiculous number of tokens. I’m a data engineer and I’ve spent a lot of time looking at messy documents, project knowledge bases and RAG setups, so I’ve started paying more attention to why this keeps happening. A lot of the time, the problem isn’t the model itself. It’s somewhere in the way the knowledge is written, split up, indexed or retrieved. So if you’re dealing with something like: “I literally told the AI this already.” “Why is it reading the wrong section?” “It has all my docs. Why is the answer still wrong?” “Why is this thing burning through so many API tokens?” Feel free to describe what you’re building and what’s going wrong. I’m happy to take a look, ask a few questions and share what I’d check first. Just don’t post any private or sensitive data obviously. I’ll pick 10 interesting ones. Let’s see how broken they are. submitted by /u/Worried-Variety3397
Originally posted by u/Worried-Variety3397 on r/ArtificialInteligence