ai-builder

name	ai-builder
description	AI Builder for Power Platform. Use when: document processing, prediction models, GPT prompt actions in flows, prebuilt models (text recognition, sentiment, entity extraction), custom models, AI Builder in Canvas Apps and Power Automate, licensing.

Skill: AI Builder

When to Use

Trigger when building, configuring, or integrating AI Builder models -- document processing, prediction, GPT prompts, prebuilt models, or custom ML models within Power Platform.

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Model Types

Model Type	Description	Training Required?
Document processing	Extract fields from invoices, receipts, custom documents	Yes -- train with sample documents
Text recognition (OCR)	Extract text from images and PDFs	No (prebuilt)
Sentiment analysis	Detect positive/negative/neutral sentiment in text	No (prebuilt)
Entity extraction	Extract entities (names, dates, addresses) from text	No (prebuilt)
Text classification	Categorize text into custom categories	Yes -- train with labeled examples
Object detection	Detect and count objects in images	Yes -- train with labeled images
Prediction	Predict outcomes based on historical data	Yes -- requires Dataverse table with history
GPT prompts	Custom prompts using Azure OpenAI GPT models	No -- prompt engineering only
Category classification	Classify items into predefined categories	Yes

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⚠️ REQUIRED: Load Sub-Files Before Implementation

SKILL.md is a summary only — it is NOT sufficient for implementation.

The detailed content (complete payloads, XML templates, working examples, edge-case handling) lives in sub-files in the same directory as this SKILL.md. Before writing any code, you MUST use read_file on the sub-files relevant to your task:

• Document Processing -- Document processing models, training, publishing, Power Automate integration, batch processing, confidence thresholds, human review loop
• GPT Prompts -- GPT prompt actions, prompt engineering for Power Platform, Power Automate and Canvas App integration, token management
• Predictions -- Prediction models, category classification, object detection, training data requirements, scheduling retraining, performance monitoring

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Licensing

Capability	License Requirement
Prebuilt models (OCR, sentiment)	Included with Power Apps/Automate Premium
Custom models (document processing, prediction)	AI Builder add-on capacity (credits)
GPT prompts	AI Builder add-on capacity (credits)
Consumption	Credit-based -- different models consume different credits per call

Tip: Start with prebuilt models (no additional cost with Premium license) before investing in custom model training.

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Anti-Patterns

• Training document processing models with fewer than 5 sample documents (poor accuracy)
• Not testing models with documents that differ from training samples
• Using GPT prompts for deterministic logic (use expressions or business rules instead)
• Hardcoding model IDs in flows -- use environment variables for environment promotion
• Not monitoring AI Builder credit consumption (can exhaust capacity unexpectedly)
• Processing sensitive data through GPT prompts without reviewing data residency policies
• Using custom models when a prebuilt model already handles the document type
• No error handling around AI Builder actions in flows (models can fail on unexpected input)

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Related Skills

• power-automate -- AI Builder actions are most commonly used in cloud flows
• canvas-apps -- AI Builder components for in-app document processing and predictions
• dataverse -- Prediction models train on Dataverse table data; results stored in Dataverse
• alm -- AI Builder models are solution-aware and promote with managed solutions
• copilot-studio -- GPT prompts can be used as plugin actions in Copilot agents
• azure-openai -- When AI Builder GPT limits are insufficient, escalate to direct Azure OpenAI