LLM systems
Architecture notes on RAG, agents, tool boundaries, structured outputs, and review loops.
Expect notes that connect implementation details to product judgment: what to test, what to monitor, where human review belongs, and how to move from a promising prototype to a system people can trust.
The newsletter follows the same themes as the latest writing: causal product measurement, uplift modeling, AI evaluation, and production AI systems.
Why high-accuracy churn prediction can improve renewal rate while losing revenue, and how uplift thinking targets incremental renewals instead of risk scores.
Read articleA practical guide to CATE, meta-learners, Qini, AUUC, decile lift, online experiments, and incremental ROI.
Read articleWhy production AI agents need custom eval sets, trajectory checks, calibrated judges, regression tests, and business-ready metrics.
Read article