What is Language Understanding (LUIS)?


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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.

A client application for LUIS is any conversational application that communicates with a user in natural language to complete a task. Examples of client applications include social media apps, AI chatbots, and speech-enabled desktop applications.

Conceptual image of 3 client applications working with Cognitive Services Language Understanding (LUIS)

Use LUIS in a chat bot

Once the Azure LUIS app is published, a client application sends utterances (text) to the LUIS natural language processing endpoint API and receives the results as JSON responses. A common client application for LUIS is a chat bot.

Conceptual imagery of LUIS working with Chat bot to predict user text with natural language understanding (NLP)

Step Action
1 The client application sends the user utterance (text in their own words), "I want to call my HR rep." to the LUIS endpoint as an HTTP request.
2 LUIS enables you to craft your custom language models to add intelligence to your application. Machine learned language models take the user's unstructured input text and returns a JSON-formatted response, with a top intent, HRContact. The minimum JSON endpoint response contains the query utterance, and the top scoring intent. It can also extract data such as the Contact Type entity.
3 The client application uses the JSON response to make decisions about how to fulfill the user's requests. These decisions can include decision tree in the bot framework code and calls to other services.

The LUIS app provides intelligence so the client application can make smart choices. LUIS doesn't provide those choices.

Natural language understanding (NLU)

LUIS provides artificial intelligence (AI) in the form of NLU, a subset of natural language processing AI.

Your LUIS app contains a domain-specific natural language model. You can start the LUIS app with a prebuilt domain model, build your own model, or blend pieces of a prebuilt domain with your own custom information.

  • Prebuilt model LUIS has many prebuilt domain models including intents, utterances, and prebuilt entities. You can use the prebuilt entities without having to use the intents and utterances of the prebuilt model. Prebuilt domain models include the entire design for you and are a great way to start using LUIS quickly.

  • Custom model LUIS gives you several ways to identify your own custom models including intents, and entities. Entities include machine-learning entities, specific or literal entities, and a combination of machine-learning and literal.

Learn more about NLP AI, and the LUIS-specific area of NLU.

Step 1: Design and build your model

Design your model with categories of user intentions called intents. Each intent needs examples of user utterances. Each utterance can provide data that needs to be extracted with machine-learning entities.

Example user utterance Intent Extracted data
Book a flight to Seattle? BookFlight Seattle
When does your store open? StoreHoursAndLocation open
Schedule a meeting at 1pm with Bob in Distribution ScheduleMeeting 1pm, Bob

Build the model with the authoring APIs, or with the LUIS portal, or both. Learn more how to build with the portal and the SDK client libraries.

Step 2: Get the query prediction

After your app's model is trained and published to the endpoint, a client application (such as a chat bot) sends utterances to the prediction endpoint API. The API applies the model to the utterance for analysis and responds with the prediction results in a JSON format.

The minimum JSON endpoint response contains the query utterance, and the top scoring intent. It can also extract data such as the following Contact Type entity and overall sentiment.

    "query": "I want to call my HR rep",
    "prediction": {
        "topIntent": "HRContact",
        "intents": {
            "HRContact": {
                "score": 0.8582669
        "entities": {
            "Contact Type": [
        "sentiment": {
            "label": "neutral",
            "score": 0.5

Step 3: Improve model prediction

After your LUIS app is published and receives real user utterances, LUIS provides active learning of endpoint utterances to improve prediction accuracy. Review these suggestions as part of your regular maintenance work in your development lifecycle.

Development lifecycle and tools

LUIS provides tools, versioning, and collaboration with other LUIS authors to integrate into the full development life cycle.

Language Understanding (LUIS), as a REST API, can be used with any product, service, or framework with an HTTP request. LUIS also provides client libraries (SDKs) for several top programming languages. Learn more about the developer resources provided.

Tools to quickly and easily use LUIS with a bot:

  • LUIS CLI The NPM package provides authoring and prediction with as either a stand-alone command-line tool or as import.
  • LUISGen LUISGen is a tool for generating strongly typed C# and typescript source code from an exported LUIS model.
  • Dispatch allows several LUIS and QnA Maker apps to be used from a parent app using dispatcher model.
  • LUDown LUDown is a command-line tool that helps manage language models for your bot.

Integrate with a bot

Use the Azure Bot service with the Microsoft Bot Framework to build and deploy a chat bot. Design and develop with the graphical interface tool, Composer, or working bot samples designed for top bot scenarios.

Integrate with other Cognitive Services

Other Cognitive Services used with LUIS:

  • QnA Maker allows several types of text to combine into a question and answer knowledge base.
  • Speech service converts spoken language requests into text.

LUIS provides functionality from Text Analytics as part of your existing LUIS resources. This functionality includes sentiment analysis and key phrase extraction with the prebuilt keyPhrase entity.

Learn with the Quickstarts

Learn about LUIS with hands-on quickstarts using the portal and the SDK client libraries.

Deploy on premises using Docker containers

Use LUIS containers to deploy API features on-premises. These Docker containers enable you to bring the service closer to your data for compliance, security or other operational reasons.

Next steps