Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
Embeddings turn text into numbers a computer can compare. Here's the working mental model, a runnable Python example, and where embeddings fit in real apps.
A hands-on intro to prompt engineering. Learn the four levers (role, format, examples, constraints) and watch a vague prompt turn into a reliable one.
A working retrieval-augmented generation app you can run today. Markdown ingestion, embeddings, semantic search, and an LLM answer — start to finish in one afternoon.
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 practical embedding model upgrade guide for RAG systems, built from a real support-search migration that initially reduced answer quality instead of improving it.
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.
