Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
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.
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.
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.
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.
