What is Azure AI Foundry Tools?
Azure AI Foundry Tools is the toolset within the AI Foundry platform for developing AI applications. The tools include Prompt Flow for visual workflow development, Playground for interactive model testing, Evaluation Framework for systematic quality measurement, and deployment tools for production operations.
The centerpiece is Prompt Flow: A visual editor where you connect nodes to create complex LLM workflows. Nodes can be LLM calls, Python code, Azure AI Search queries, or external API calls. The finished flow is deployed as a REST API or Azure Function.
Core Features
- Prompt Flow for visual workflow development
- Playground for interactive model testing
- Evaluation Framework with built-in and custom metrics
- Data Connectors for Azure Storage, SharePoint, databases
- Deployment as REST API or Azure Function
Typical Use Cases
AI developers use Prompt Flow to visually create RAG pipelines. A typical flow: Enter user query, query Azure AI Search for relevant documents, pass documents as context to LLM, generate response.
Data scientists compare models in the Playground. GPT-4, GPT-4o, and Llama are tested with the same prompts, responses are evaluated, the best option for the use case is identified.
DevOps teams deploy finished flows as scalable REST APIs. CI/CD pipelines integrate evaluation runs to validate quality before deployment.
Benefits
- Accelerated development through visual tools
- Consistent quality through integrated evaluation
- Seamless transition from development to production
- Reusable flows and components
Integration with innFactory
As a Microsoft Solutions Partner, innFactory supports you with Azure AI Foundry Tools: Prompt Flow development, RAG architecture, evaluation strategy, and production deployment.
Typical Use Cases
Frequently Asked Questions
What is included in Prompt Flow?
A visual editor for creating LLM workflows with nodes for prompts, Python code, Azure AI Search, and APIs. Flows can be versioned and deployed as REST APIs.
How does model comparison work?
In the Playground, you can send the same prompt to different models and compare responses. Evaluation datasets enable systematic comparison with metrics.
Can I use Foundry Tools locally?
Yes, VS Code Extension and Azure AI CLI enable local development. Flows are deployed in Azure but can be tested locally.
How do I integrate my own data for RAG?
Via Data Connectors to Azure Blob Storage, SharePoint, Azure SQL, or APIs. Documents are indexed in Azure AI Search and made available for retrieval.
