Shipping AI Features Faster: A Lightweight Analytics Loop
A 5-step feedback loop using event design, cohort tracking, and rapid iteration to keep AI feature development grounded in adoption and retention—not hype.
A curated collection of concise, practitioner-focused articles exploring analytics engineering, product experimentation, cloud data workflows, and practical AI adoption. Ideas that move from prototype to production—without the fluff.
A 5-step feedback loop using event design, cohort tracking, and rapid iteration to keep AI feature development grounded in adoption and retention—not hype.
How to reduce clutter, reclaim performance, and focus stakeholders on decision-driving metrics using a SCORE pruning model.
Practical patterns for schema evolution, late-arriving data, and replay safety in product analytics pipelines.
A structured checklist to add value early while building context—used across SaaS, higher-ed, and platform teams.
Practical levers (chunking, embeddings, freshness layering) that improve answer precision before you swap models.
A pre-build process using question trees + narrative zones to cut iteration cycles and deliver adoption-ready dashboards.