Insights
Technical notes for AI reliability decisions.
Short, practical notes on eval design, RAG regressions, agent failure modes, and release gates. Examples are generic unless a page explicitly says approved client evidence is being used.
Reviewed Jul 12, 2026
How to Decide Whether an AI Workflow Ships, Changes, or Stops
A decision framework for AI teams that need evidence before shipping, revising, or stopping an agent, RAG, or model workflow.
Reviewed Jul 12, 2026
RAG Regression Testing: What to Track Before Retrieval Changes
A concise checklist for tracking RAG regressions across retrieval, context assembly, answer quality, and release gates.
Reviewed Jul 12, 2026
Failure Taxonomies for Agent Workflows
How to group agent failures by trigger, owner, severity, and release impact so evaluation results lead to engineering action.
Reviewed Jul 12, 2026
Release Gates for LLM Features
How to define blockers, warnings, thresholds, and decision records before releasing LLM-backed features.
Reviewed Jul 12, 2026
What an Evaluation Plan Should Contain
The minimum useful contents of an AI evaluation plan: target behavior, risks, inputs, replay rows, thresholds, and decision owner.
Reviewed Jul 12, 2026
Why Offline Scores Miss Production AI Failures
Why offline metrics can miss production AI failures, and how replay rows and failure taxonomies make evaluation more actionable.