Most monitoring conversations are about thresholds and routing - how many failures before we restart something, how long before we page someone, who gets woken up at 3am. That's real work. But it quietly assumes something worth verifying: that the signal you're measuring actually means what
Every RAG tutorial follows almost the same flow: convert the user's query into an embedding, retrieve the Top-K most similar documents, and pass them to the LLM. The part that's usually skipped is a simple question: Why K? Why retrieve 3 documents instead of 1? Why
Search is usually treated as a solved problem. Store the data, index the field you plan to query on, look it up. That holds up perfectly well right until the moment the words someone actually types don't match the words you indexed. The moment exact-match search quietly fails
Most SEO conversations start and end with content - better keywords, longer articles, more backlinks. That's a real part of the picture, but it quietly assumes something worth actually verifying: that search engines, and increasingly AI answer engines, can find your pages, read them correctly, and trust what