Adding a vector database is usually the first move when output quality drops on a knowledge-heavy task. It's rarely the right one. Most quality problems in that category aren't retrieval failures, they're curation failures wearing a retriev…
Noticed this after blaming a model for a string of bad outputs on a task I'd been running for weeks. Same model, same general request, quality visibly declining. Went back and compared my actual messages over that period instead of assuming model d…
Kept running into a specific failure pattern across different AI-assisted debugging sessions and didn't have a clean way to describe it until I actually sat down and compared a few of them side by side. The pattern: an error goes away, I file the p…
Someone explained this to me in a comment thread and it's been rattling around in my head since. The idea: in a long conversation, if the model says something wrong and you correct it, that correction doesn't necessarily erase the wrong idea…
Not a research post, just something I've been chewing on after watching this happen a few times now. When a human reviewer leaves a comment saying "this looks like a bug," people push back, ask questions, sometimes just disagree outright….
Noticed a pattern: people switch from GPT to Claude, upgrade to a newer version, try a bigger model and the output barely changes. If that's happened to you, the issue usually isn't the model. It's what you handed it before asking the quest…
