Practical writing on engineering growth, system design, and shipping real products.
Most LLM prototypes fail at the same transition: turning a clever demo into a reliable user-facing feature. This checklist focuses on the engineering decisions that make LLM functionality predictable, testable, and supportable in production.
Most LLM failures in products aren’t model failures—they’re interface, data, and lifecycle failures. This post lays out a practical blueprint for building LLM features that behave predictably, degrade gracefully, and are testable end-to-end.
RAG failures are usually retrieval failures: the model can’t use what it can’t find. This post breaks down pragmatic retrieval design patterns—chunking, indexing, filtering, and reranking—so answers are grounded and repeatable.
Moving from a working notebook to a reliable product is mostly engineering work: interfaces, data contracts, evaluation, and deployment discipline. Here’s a checklist that helps teams ship ML/LLM capabilities without turning the system into a science project.