Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
A practitioner's guide to tracing, cost tracking, and evaluating LLM apps in production with Langfuse, Helicone, Arize Phoenix, and LangSmith.
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.
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 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.
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.
