New Section: AI R&D Journal
Practical AI writing for operators, product leaders, and engineering teams. We focus on evaluation frameworks, production reliability, governance, and measurable business results.
How high-performing teams evaluate AI outputs in production: clear decision classes, reliable scorecards, robust test sets, launch gates, drift monitoring, and incident-driven learning.
A practical guide to choosing between pre-trained models, fine-tuning, and training from scratch—using concrete examples, decision criteria, and trade-offs across quality, cost, speed, and control.
An implementation-first guide to system prompt tuning with rigorous methodology, comparative benchmarks, and practical templates to improve quality, safety, and efficiency.
A practical guide for teams searching for an AI consultancy or agentic consultant to decide between agentic and non-agentic implementation patterns with measurable ROI.
A practical framework for designing AI evaluations when you need help building AI and help building LLM systems across deterministic workflows and subjective assistant experiences.
How to use NDCG to measure ranking quality while improving retrieval quality during AI implementation and LLM search rollouts.
A practical playbook for teams that need help building LLM quality systems, tuning judge prompts, and improving reviewer agreement in production.
Begin with our guide to designing production-ready AI evaluations. Read “AI Evals: How They Should Work.”