prompt-iteration-and-diagnostics

name	prompt-iteration-and-diagnostics
description	Diagnose failed AI image or video outputs, identify root causes, revise prompts with single-variable iteration, and produce structured prompt revision reports.

prompt-iteration-and-diagnostics

When to use

• The user shows or describes a failed output and asks how to fix it.
• The user wants prompt diagnostics, iteration, A/B variants, failure analysis, or a revision report.
• The output has drift, bad anatomy, wrong motion, weak realism, camera chaos, text/logo errors, prompt collapse, or model transfer failure.
• The prose, dialogue, or film package has generic AI voice, weak story logic, rights/provenance gaps, or release-quality concerns.

When not to use

• The user has no output or failure description and only needs first-draft prompting.
• The issue is a tool outage or account/billing problem.
• The user asks for unsafe bypass instructions.

Required inputs

• Original prompt
• Observed output or failure description
• Target model/tool
• Desired result

Optional inputs

• Reference images
• Settings/parameters
• Seed/version
• Previous attempts
• Continuity bible

Workflow

1. Restate the intended result and the actual failure.
2. Classify failure: prompt ambiguity, contradiction, overload, model limitation, continuity drift, temporal overload, reference conflict, safety/policy rejection, or parameter mismatch.
3. Identify the smallest change likely to improve the result.
4. Apply the one-variable rule for iterative tests unless the prompt is fundamentally broken.
5. Rewrite using the relevant skill: image, video, continuity, style, or model-adaptation.
6. Produce a revision report with diagnosis, changed fields, unchanged anchors, expected improvement, and next test.
7. When the failure is a model limitation, redesign the shot or route to a better model instead of forcing the same prompt.

Decision logic

• If output is 80 percent correct, make minimal edits.
• If identity drift appears, strengthen references and bible anchors.
• If motion fails, reduce actions and camera moves.
• If text/logos fail, choose a text-capable model or simplify typography.
• If a prompt is rejected, remove unsafe/IP-sensitive content and do not provide bypass tactics.

Output formats

• Failure diagnosis
• Revised prompt
• A/B test variants
• Prompt revision report
• Continuity update recommendations
• Model-routing note
• Story/voice/release QC report

Quality checks

• Diagnosis maps to a specific prompt or model cause.
• Revisions preserve what already worked.
• Only meaningful variables change per test.
• The report distinguishes fixable prompt issues from model limitations.
• Story, voice, continuity, performance, rights, and provenance checks are separated when evaluating a full AI film package.
• No unsafe bypass methods are included.

Anti-patterns

• Rewriting the entire prompt after a small failure
• Adding long negative lists without prioritization
• Blaming the model before checking contradictions
• Ignoring uploaded reference conflicts
• Treating policy rejection as a prompt-engineering puzzle.

Exit criteria

• The user has a revised prompt and a clear next test or model-routing decision.

Supporting files

Read only the supporting file needed for the active task:

• references/iteration_diagnostics.md
• references/failure_modes.md
• references/revision_loops.md
• references/story_voice_release_qc.md
• templates/prompt_revision_report.md
• templates/failed_output_intake.md