Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
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.
A practical field manual for engineering teams who want AI features that survive real users, incidents, and budgets — not just demo day.
Use prompts to get reliable, safe outputs from LLMs for runbooks, code, and ops tasks.
