Conversational AI enables human-like interactions between users and machines through natural language. This includes voice assistants, chatbots, and other systems that process spoken or written language.

Azure provides powerful tools to build and deploy conversational experiences using a combination of language understanding and bot services.

1. Natural Language Processing (NLP)

• Technology that helps machines understand, interpret, and generate human language.
• Includes tasks like intent recognition, sentiment analysis, and entity extraction.

2. Language Understanding (LUIS)

• LUIS (Language Understanding Intelligent Service) allows you to build NLP models with minimal code.
• You define intents (what the user wants) and entities (key information).
• Example: User says “Book a flight to Madrid”

→ Intent: BookFlight

  → Entity: Destination = Madrid

3. Azure Bot Service

• A managed platform for creating intelligent bots that run on Microsoft’s infrastructure.
• Integrates with:

1. LUIS (for understanding language)
2. QnA Maker (for FAQs)
3. Microsoft Teams, web chat, and more

4. QnA Maker (now part of Azure Language Service)

• Create simple question-and-answer bots by importing FAQs or URLs.
• Useful for support bots, documentation assistants, etc.

• LUIS understands what the user means.
• QnA Maker answers common questions.
• Azure Bot Service provides the platform to connect these capabilities into a conversational app.

• A helpdesk chatbot that answers HR questions.
• A virtual agent that helps customers track orders.
• A voice-enabled assistant that schedules meetings.

You should be able to:

• Identify components used in building conversational AI.
• Match Azure services to their roles (e.g., LUIS for intent detection).
• Understand integration points between bots and external channels.

• Machine Learning in Azure
• Cognitive Services