llm-failure-modes

What problem does it solve?

Helps teams identify, analyze, and mitigate cognitive failure modes in LLM-driven reasoning, reducing errors in high-stakes decisions.

Core Features & Use Cases

• Failure mode taxonomy: Provides a structured taxonomy of common cognitive failures in LLMs.
• Editorial guidelines: Offers checklists and best practices for evaluating model outputs.
• Practical validation: Enables post-hoc analysis and design of guardrails for reliable reasoning in engineering contexts.
• Use Case: In critical data science tasks, use this guide to audit the model's reasoning path and surface potential missteps.

Quick Start

Apply the failure-mode checklist to the model's response and record any flagged issues for corrective action.