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Developing a Chatbot Using Generative Ai

This guide explains how Generative AI is revolutionizing chatbots by enabling human-like conversations, automation, and intelligent workflows. It outlines key development steps, the role of agentic AI, and why managers need specialized training like a Generative AI course for managers and an agentic AI course to lead AI-driven initiatives effectively.

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Developing a Chatbot Using Generative Ai

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  1. Developing a Chatbot Using Generative AI A Manager's Guide to Building AI-Powered Conversational Solutions

  2. Why Generative AI is Changing Chatbots • Traditional chatbots relied on scripts and decision trees, but Generative AI changes this by: • - Understanding context rather than just keywords • - Generating dynamic language like a human • - Adapting tone and personality to the situation • Used widely in healthcare, e-commerce, education, and finance.

  3. Steps to Develop a Generative AI Chatbot 1. Define Business Goals: Reduce service load, drive leads, improve engagement. 2. Select the Right Generative AI Models: Choose models optimized for your use case. 3. Create a Scalable Data Pipeline: Ensure clean, unbiased, and governed data.

  4. Steps to Develop a Generative AI Chatbot (Contd.) 4. Integrate with Existing Systems: CRMs, HR, e-commerce for better functionality. 5. Test and Iterate: Continuous improvement through feedback and data updates.

  5. Why Managers Need Generative AI Learning • Generative AI course for managers teaches: - Business integration strategies - Risk and ethics management - Leading cross-functional AI teams • Gen AI course for managers focuses on practical deployment and oversight.

  6. Role of Agentic AI in Chatbots • Agentic AI adds reasoning and task execution: - Automates product ordering and scheduling - Generates reports instantly • Agentic AI course helps managers design intelligent, autonomous workflows.

  7. Benefits of Generative AI Training • Generative AI training programs help managers: - Evaluate vendor solutions effectively - Set realistic ROI expectations - Understand AI adoption roadmaps in their sector.

  8. Challenges and Solutions • Challenges: - AI hallucinations - Data privacy and compliance - Legacy system integration • Solutions from Generative AI training: - Reinforcement learning - Governance models - Human-in-the-loop approaches.

  9. Final Thoughts • Generative AI chatbots redefine business communication. • Managers trained via Generative AI course for managers and agentic AI course will lead this shift. • Question is not 'Should we adopt AI?' but 'How quickly can we adapt?'

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