Machine learning, LLM operations, and practical AI engineering.
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.
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.
AI Inference Cost Optimization. Practical guidance for reliable, scalable platform operations.
