If you have ever run a large system, you know the feeling. One misbehaving service starts consuming CPU. Latency spikes. Suddenly, unrelated parts of the system slow down. What should
Senior engineers building retrieval augmented generation systems often start with a clean mental model. Embed documents, embed the query, run a vector search, and return the nearest neighbors. It works
Your AI system is shipping features faster than ever. Offline benchmarks look great. Evaluation dashboards trend upward every week. And yet production tells a different story. Support tickets spike. User
You usually do not notice operational drift when it starts. The system still passes health checks. Latency looks mostly normal (though subtle warning signs are often present — see seven
Most AI prototypes look impressive in a notebook. The model predicts well on a curated dataset. Latency feels fine on a developer laptop. A demo convinces stakeholders that the hard
Your AI system behaves perfectly in staging. The guardrails block unsafe prompts, policy filters trigger exactly where you expect, and the red team report looks clean. Then real users arrive.
Search looks simple from the outside. A user types a few words, hits Enter, and results appear in milliseconds. Under the hood, that request kicks off a distributed system that
Senior engineers eventually discover a hard truth about technical leadership. The architecture diagram might say you own the system, but that does not mean anyone will follow your decisions. Conversely,
Most RAG systems look impressive in demos and fragile in production. The pattern is familiar. Retrieval works on a curated dataset, latency looks acceptable under light load, and the model