llm-failure-modes

LLM cognitive failure modes encompass reasoning errors and cognitive blind spots that degrade high-stakes decision-making. This Skill provides a structured taxonomy to identify these specific failure patterns in model outputs.

Auditing an LLM reasoning path involves applying a structured failure-mode checklist to the model response to surface potential missteps. This post-hoc analysis records flagged issues for corrective action.

Yes, the failure mode taxonomy enables practical validation by guiding the design of guardrails for reliable reasoning. It provides editorial guidelines and checklists to mitigate identified cognitive failures in engineering contexts.

Evaluating LLM outputs for high-stakes product decisions is best achieved by applying structured checklists and best practices from a failure mode taxonomy. This guides critique and verification to catch cognitive blind spots.

Standard LLM outputs should not be relied upon for high-stakes reasoning without post-hoc analysis when cognitive blind spots pose risks. A structured taxonomy is needed to verify the model reasoning path and apply corrective actions.