Understanding AI Generation Limits
PraxPilot uses AI to help practitioners generate protocols, analyze cases, and refine recommendations. Depending on your plan, certain AI-powered actions may have monthly usage limits.
Understanding how these limits work can help you plan your workflow and avoid interruptions.
What Counts as an AI Generation
AI generations typically occur when PraxPilot creates or regenerates clinical content using its AI engine.
Examples include:
Generating a full protocol for a new case
Using Write with AI to regenerate or expand a section of a protocol
Generating clinical reasoning or additional analysis features
These actions use AI to produce new clinical content based on the case data.
Actions That Do Not Count Toward AI Generations
Many actions inside PraxPilot do not use AI and therefore do not count toward generation limits.
These include:
Creating or editing a case
Updating symptoms, notes, or lab markers
Editing protocol recommendations manually
Managing case status or follow-up tracking
Exporting a protocol as a PDF
These features remain available regardless of AI usage limits.
What Happens When You Reach Your Limit
If your plan has a monthly AI generation limit and you reach that limit, PraxPilot will temporarily prevent new AI-generated content from being created until the next billing cycle.
You can still:
View and edit existing protocols
Manage your cases
Export protocols
Continue working with previously generated content
Your AI generation allowance will reset at the start of the next billing cycle.
Upgrading Your Plan
If you regularly reach your AI generation limit, upgrading your plan may provide a higher monthly allowance.
Upgrading allows practitioners to continue generating protocols and using AI-assisted features without interruption.
Best Practices for Using AI Generations
To make the most of your available AI generations:
Ensure your case intake includes meaningful clinical information
Review generated protocols before regenerating sections
Use Write with AI selectively when refining specific areas of a protocol
Providing clear case context often leads to more accurate results on the first generation.