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Duration 14 hours
Course Outline
Getting Started with Mistral Conversational AI
- An overview of Mistral's conversational models
- Understanding their capabilities and constraints
- Exploring enterprise use cases for AI assistants
Utilizing Mistral Connectors
- Establishing connections with Google Drive, Docs, and Calendars
- Integrating with various SaaS tools
- Handling authentication and permission management
Retrieval-Augmented Generation (RAG)
- Understanding the principles of grounding conversational assistants
- Indexing enterprise data for retrieval
- Executing context-aware queries and generating responses
Crafting User Experiences for Assistants
- Core principles of conversational UX design
- Creating workflows for internal tools
- Developing engaging chat experiences for end customers
Integration and Deployment Strategies
- Embedding assistants seamlessly into product workflows
- Leveraging APIs and SDKs for deployment
- Executing testing and iterative improvement cycles
Performance Tracking and Monitoring
- Assessing the quality of assistant responses
- Implementing logging and analytics frameworks
- Establishing loops for continuous improvement
Case Studies and Industry Best Practices
- Insights from real-world implementation examples
- Key takeaways from enterprise deployments
- Looking ahead to the future of conversational assistants
Wrap-up and Recommendations for Next Steps
Requirements
- Fundamental knowledge of web applications and APIs
- Practical experience in software integration or full-stack development
- Basic familiarity with conversational AI or chatbot technologies
Target Audience
- Product managers
- Full-stack developers
- Integration engineers
