Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
We've shipped all three patterns to production. They're not interchangeable. Here's the framework we now use to decide which approach fits a given task.
We invalidate ~6% of LLM outputs before they reach a downstream system. Here's how we structure prompts and validators to catch malformed responses early.
We ran the same RAG workload across three vector stores for a quarter each. Here's what we learned about latency, cost, and operational overhead.
We ran the same workload on both for half a year. The break-even point isn't where most blog posts say it is — and the latency story has more nuance than throughput-per-dollar charts admit.
Six months running RAG in production taught us that the retrieval step matters far more than the model. Concrete techniques that moved the needle, with before/after numbers.
Battle-tested prompt patterns from running LLM features in production: structured output, chain-of-thought, and graceful failure handling.
A real-world model fallback guide for customer-facing AI systems, covering how one team preserved response quality and support SLAs during a partial provider degradation.
A real-world guide to prompt versioning and regression testing for production AI features, focused on preventing the subtle changes that hurt quality long before anyone notices.
A search-friendly guide to RAG retrieval quality evaluation, based on the moment one production assistant started citing stale documents and the team had to prove what 'good retrieval' meant.
A field report from rolling out retrieval-augmented generation in production, including cache bugs, bad embeddings, and how we fixed them.
A practical production playbook for AI systems: evaluation gates, guardrails, observability, cost control, and reliable release management.
