Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
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.
A million-token window doesn't retire your retrieval stack. Here's when to stuff the prompt, when to retrieve, and when to do both.
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 single answer-quality score hides where your RAG pipeline actually breaks. Split retrieval eval from generation eval and measure each one honestly.
A RAG system that invents facts erodes trust fast. Here is how we ground answers, force citations, and catch the fabrications before users do.
A 180k-token context window is not a license to stuff everything in. Here's how we cut prompt size 60% without hurting answer quality, and what to trim first.
When our single LLM provider had a 40-minute outage, every AI feature went dark. A gateway with routing and fallback fixed that, and cut spend 30% as a bonus.
Users hit stop, but our server kept paying for tokens for another 40 seconds. Here's how we wired real cancellation and backpressure into an SSE streaming endpoint.
The top model on the MTEB leaderboard made our search worse and our bill bigger. Here's how we actually picked an embedding model for a real RAG system.
Most agents that "need memory" actually need a smaller context window and a database. Here's how we cut a support agent's token bill by 60 percent by deleting memory.
A user got our support bot to recite its system prompt and then draft a refund it wasn't authorized to give. Two layers of guardrails, one on input, one on output, closed both holes.
