Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
Once you call more than one LLM provider, a gateway saves you from reinventing routing, fallback, caching, and spend limits in every service.
A practitioner's guide to how LLM API pricing works, how to estimate a workload's monthly bill, and the levers that actually cut it.
A practitioner's guide to tracing, cost tracking, and evaluating LLM apps in production with Langfuse, Helicone, Arize Phoenix, and LangSmith.
LangChain orchestrates agents and integrations, LlamaIndex owns retrieval and RAG. Here's where they overlap, where they don't, and which to reach for.
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 practitioner's guide to picking a GPU cloud for training and inference, where hourly rates for the same H100 can differ by 3x.
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.
When RAG answers go sideways, the model usually isn't the problem. Here's the top-to-bottom checklist we run to find where retrieval actually breaks.
A practitioner's guide to picking the right LLM API for coding, long docs, cheap extraction, reasoning, vision, voice, and on-device work.
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.
A practitioner's guide to picking between the three frontier LLM APIs based on task, price, latency, and enterprise terms.
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.
