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Course Outline

Foundations of Mistral Conversational AI

  • Overview of Mistral’s conversational model capabilities
  • Identifying strengths and limitations
  • Enterprise use cases for AI assistants

Utilizing Mistral Connectors

  • Linking with Google Drive, Docs, and Calendars
  • Integrating third-party SaaS applications
  • Handling authentication and access permissions

Retrieval-Augmented Generation (RAG)

  • Techniques for grounding conversational responses
  • Indexing enterprise data sources
  • Generating context-aware queries and answers

Crafting Assistant User Experiences

  • Core principles of conversational UX design
  • Structuring flows for internal productivity tools
  • Developing engaging customer-facing chat interfaces

Integration and Deployment Strategies

  • Embedding assistants into existing product workflows
  • Leveraging APIs and SDKs for deployment
  • Managing testing and iterative improvement cycles

Performance Evaluation and Monitoring

  • Assessing the quality of AI responses
  • Implementing logging and analytics solutions
  • Establishing continuous improvement feedback loops

Case Studies and Industry Best Practices

  • Insights from real-world implementation examples
  • Key lessons from enterprise deployments
  • Emerging trends in conversational assistant technology

Conclusion and Future Directions

Requirements

  • Working knowledge of web applications and APIs
  • Background in software integration or full-stack development
  • Basic familiarity with conversational AI or chatbots

Target Audience

  • Product managers
  • Full-stack developers
  • Integration engineers
 14 Hours

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