Lessons tagged “error-correction”
16 published lessons with this tag.
Invalidation is more informative than validation
Finding out your schema is wrong teaches you more than confirming it is right.
All systems produce errors
No process works perfectly every time — error correction must be built in from the start.
Distinguish error types
Execution errors knowledge errors and judgment errors require different correction approaches.
Fail fast fail cheap
Design systems that surface errors early when they are easiest and cheapest to correct.
Root cause analysis for recurring errors
When the same error happens repeatedly fix the root cause not just the symptom.
Post-action reviews
Reviewing what happened after completing a task surfaces errors for future correction.
Graceful degradation
Design your systems to fail partially rather than completely.
Recovery procedures
For every important process have a documented way to recover from common failures.
The blame instinct prevents learning
Focusing on who caused an error prevents understanding why it happened.
Error patterns reveal system weaknesses
Recurring errors point to structural problems not personal failures.
Error correction has a cost
Every correction takes time and energy — reduce the error rate rather than just correcting faster.
Feedback from errors is the most valuable feedback
Errors teach you more about your systems than successes do.
Self-correcting systems are the ultimate goal
The best systems detect and correct their own errors without manual intervention.
Supervise exceptions, not output
Toyota's jidoka principle — automation with a human touch — builds machines that stop the line and signal when something is out of spec, so humans supervise exceptions instead of inspecting everything. Applied to AI collaboration: define out-of-spec explicitly (missing provenance, no falsifier, contradiction flagged, format broken), let mechanical gates check every output, and spend your attention only on what stops the line.
A gate that cries wolf becomes noise
Validation gates are themselves signal sources — and a gate with a high false-alarm rate becomes a noise source that trains you to ignore it. Aviation and intensive care call the result alarm fatigue, and it kills. A self-healing filter network therefore needs a meta-gate: every gate's alarms get logged, its false-positive rate reviewed, and chronic criers retuned or removed. The system must audit not just its contents but its own filters.
Verification debt
Every AI output you accept without validation is verification debt: trust extended without collateral. Like financial debt it is sometimes the right trade — speed now, checking later — but it compounds quietly, concentrates in the claims you reuse most, and eventually some decision defaults on it. The discipline is not zero debt; it is visible debt: know what is unverified, cap it, and pay it down on the claims your decisions actually load-bear.