When AI gives you bad results, the failure almost always comes from the instruction — not the model. These are the six most common structural failures in AI prompts and workflow instructions, with specific causes and fixes for each. If your AI keeps getting it wrong, start here.
Diagnose your prompt free — 10 runs includedAI generates plausible-sounding but false information. Why it happens — and the exact instruction changes that stop it.
The #1 cause of generic AI output. Four specificity gaps that make AI answer broadly instead of precisely.
AI works from what you give it. When context is absent it assumes — and those assumptions are usually wrong.
AI gives the right content in the wrong structure. How to specify format so it sticks.
Instructions that worked last month have stopped working. What causes it and how to diagnose it.
AI agents take actions outside their intended scope. How to set boundaries and approval gates before it matters.
TryPromptFlow's Workflow Doctor diagnoses these failure patterns automatically — checking completeness, specificity, executability, constraint fidelity, and test coverage across your instructions before they reach production. For multi-step AI agents, Agentic Workflow Doctor reviews the full workflow design, approval gates, and action boundaries.