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Duration 14 hours
Course Outline
Introduction to Mistral Conversational AI
- Overview of Mistral’s conversational models.
- Understanding capabilities and limitations.
- Identifying use cases for assistants within enterprises.
Utilising Mistral Connectors
- Connecting to Google Drive, Docs, and Calendars.
- Integrating with various SaaS tools.
- Managing authentication and permission settings.
Retrieval-Augmented Generation (RAG)
- Core concepts for grounding conversational assistants.
- Indexing enterprise data effectively.
- Querying systems and responding with relevant context.
Designing User Experiences for Assistants
- Key principles of conversational UX design.
- Creating flows for internal tools.
- Developing customer-facing chat experiences.
Integration and Deployment
- Embedding assistants into existing product workflows.
- Utilising APIs and SDKs for deployment.
- Executing testing and iteration cycles.
Performance and Monitoring
- Evaluating the quality of responses.
- Implementing logging and analytics.
- Establishing continuous improvement loops.
Case Studies and Best Practices
- Insights from real-world implementations.
- Lessons learned from enterprise deployments.
- Exploring future directions for conversational assistants.
Summary and Next Steps
Requirements
- A solid understanding of web applications and APIs.
- Practical experience in software integration or full-stack development.
- Familiarity with conversational AI concepts or chatbot technology.
Target Audience
- Product managers.
- Full-stack developers.
- Integration engineers.
