| 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.
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 |
⚠️ 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
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.
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)
Related Skills
power-automate -- AI Builder actions are most commonly used in cloud flowscanvas-apps -- AI Builder components for in-app document processing and predictionsdataverse -- Prediction models train on Dataverse table data; results stored in Dataversealm -- AI Builder models are solution-aware and promote with managed solutionscopilot-studio -- GPT prompts can be used as plugin actions in Copilot agentsazure-openai -- When AI Builder GPT limits are insufficient, escalate to direct Azure OpenAI
