AI & ML Engineering
Building and shipping systems whose behaviour is probabilistic: LLM application architecture, retrieval, evaluation, agents, inference economics and the operational discipline underneath.
Ship a non-deterministic system you can still be accountable for.
Foundational
2The Demo To Production Gap
A convincing prototype is a small fraction of the work. What sits between a demo that worked and a system you can operate, and how to reset executive expectations after they have seen the demo.
9 min readexpectationsproduction readinessThe Three Jobs Of Labelling
One ordinary word doing three unrelated jobs — measuring, teaching and operating — with opposite requirements. The question that separates them, why mixing measurement data into training flatters rather than breaks, and how to read a number somebody else produced.
8 min readlabellingevaluation
Practitioner
3Evaluation Harnesses For LLM Systems
The piece almost every team skips. How to build a golden set, design a rubric, use LLM-as-judge without being fooled by it, and turn "is this better?" from an argument into a measurement.
9 min readevaluationgolden datasetsRAG Architecture That Survives Production
Retrieval quality sets the ceiling on answer quality, and chunking decides retrieval quality. Hybrid search, reranking, grounding and citation, re-indexing, and how to tell which half of the system broke.
10 min readragretrievalTesting Non-Deterministic Systems
Assertion-based testing assumes one correct output. Model-backed systems do not have one. What replaces equality checks, where deterministic tests still belong, and what the test pyramid becomes.
9 min readtestingnon-determinism
Other domains