14 articles tagged with Production.
A green readiness probe means the probe endpoint answered, nothing more. The gaps that cause 502s during rollouts, and the Deployment settings that close them.
A demo RAG app is easy; one users trust is not. This is the map for reliable retrieval-augmented generation: grounding, evaluation, retrieval quality, guardrails, and safe rollout.
A prompt tweak or model bump can quietly wreck answers everywhere. Ship LLM changes the way you ship risky code: gate, shadow, canary, roll back.
Prompt injection is not a prompt bug, it is an architecture problem. Here is how we design LLM apps so a poisoned document cannot hijack them.
A RAG system that invents facts erodes trust fast. Here is how we ground answers, force citations, and catch the fabrications before users do.
Everyone says Compose is for dev only. We ran it in production for two years on a single node and it was the right call, until the day it very much wasn't.
A support bot that recites its system prompt and drafts refunds it isn't authorized to give has two holes. Two layers of guardrails, one on input, one on output, close both.
Our node image shipped 240 CVEs, most from OS packages we never called. Moving to distroless dropped the count to single digits and cut image size by 70%.
Users ask the same questions in slightly different words, and you pay full price every time. Semantic caching answers the repeats for almost nothing.
A long, stable system prompt re-billed on every request is money on fire. How prompt caching works, where the cache boundary belongs, and the structuring discipline that got us a big cost and latency cut without changing behavior.
Node upgrades, autoscaler scale-downs, and spot reclaims all drain nodes. Without PDBs they can take all your replicas at once. The budgets, probes, and graceful-shutdown handling that keep voluntary disruptions invisible to users.
Parsing model output with a regex and a prayer doesn't survive contact with traffic. The validation layers that keep structured LLM output reliable: constrained decoding, schema validation, and the repair loop.
