Concepts
The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types
Monitor your error detection system itself — track what…
Build error detection infrastructure that monitors both your primary outputs and your detection system's own performance, tracking what errors you catch versus what you miss through other means to detect detection failures.
Checklists are 5-10 items that catch what competent people…
Limit operational checklists to 5-10 items focused exclusively on steps most likely to be skipped or forgotten under load, not comprehensive process documentation.
Automate pattern-based error detection before manual…
Deploy automated grammar checkers, linters, or mechanical validation tools before manual review to catch pattern-based errors, reserving human attention for contextual judgment that tools cannot provide.
Three verification layers
Design verification as three independent layers—continuous signals (daily metrics), periodic samples (weekly/monthly spot-checks), and infrequent structural audits (quarterly full reviews)—with each layer optimized for different failure detection at different resource costs.
Scale AI output verification to stakes
For high-stakes AI outputs, adopt a three-tier verification intensity: skim for low-stakes brainstorming, spot-check key claims for medium-stakes communications, and verify every substantive claim for high-stakes published or production content.