What is Language Understanding (LUIS)?
Language Understanding (LUIS) is a cloud-based conversational AI service that applies custom machine-learning intelligence to a user's conversational, natural language text to predict overall meaning, and pull out relevant, detailed information. LUIS provides access through its custom portal, APIs and SDK client libraries.
This documentation contains the following article types:
- Quickstarts are getting-started instructions to guide you through making requests to the service.
- How-to guides contain instructions for using the service in more specific or customized ways.
- Concepts provide in-depth explanations of the service functionality and features.
- Tutorials are longer guides that show you how to use the service as a component in broader business solutions.
What does LUIS Offer
- Simplicity: LUIS offloads you from the need of in-house AI expertise or any prior machine learning knowledge. With only a few clicks you can build your own conversational AI application. You can build your custom application by following one of our quickstarts, or you can use one of our prebuilt domain apps.
- Security, Privacy and Compliance: Backed by Azure infrastructure, LUIS offers enterprise-grade security, privacy, and compliance. Your data remains yours; you can delete your data at any time. Your data is encrypted while it’s in storage. Learn more about this here.
- Integration: easily integrate your LUIS app with other Microsoft services like Microsoft Bot framework, QnA Maker, and Speech service.
- Build an enterprise-grade conversational bot: This reference architecture describes how to build an enterprise-grade conversational bot (chatbot) using the Azure Bot Framework.
- Commerce Chatbot: Together, the Azure Bot Service and Language Understanding service enable developers to create conversational interfaces for various scenarios like banking, travel, and entertainment.
- Controlling IoT devices using a Voice Assistant: Create seamless conversational interfaces with all of your internet-accessible devices-from your connected television or fridge to devices in a connected power plant.
Application Development life cycle
- Plan: Identify the scenarios that users might use your application for. Define the actions and relevant information that needs to be recognized.
- Build: Use your authoring resource to develop your app. Start by defining intents and entities. Then, add training utterances for each intent.
- Test and Improve: Start testing your model with other utterances to get a sense of how the app behaves, and you can decide if any improvement is needed. You can improve your application by following these best practices.
- Publish: Deploy your app for prediction and query the endpoint using your prediction resource. Learn more about authoring and prediction resources here.
- Connect: Connect to other services such as Microsoft Bot framework, QnA Maker, and Speech service.
- Refine: Review endpoint utterances to improve your application with real life examples
Learn more about planning and building your application here.