GenAI for Customer Support training course for professionals

1. Introduction to Generative AI in Customer Support

• Overview of Generative AI and its evolution

• Benefits and challenges of using AI in customer service

• Key concepts: LLMs, prompt engineering, automation

2. Prompt Engineering for Customer Interaction

• What is prompt engineering and why it matters

• Techniques to write effective prompts for support scenarios

• Practice sessions using ChatGPT for real-time support tasks

3. Building Custom GPT Models

• Collecting and structuring customer service data

• Steps to fine-tune or customise a GPT model

• Deploying models to generate support ticket replies

4. AI-Powered Email Automation using Google’s Gem

• Introduction to Google’s GenAI tools, including Gemini/Gem

• Setting up AI-based workflows for email replies

• Automating follow-ups and FAQs via Gmail integration

5. Visual AI with Anthropic for Product Issue Detection

• Understanding Anthropic’s AI tools and visual analysis

• Using AI to detect product defects and suggest solutions

• Applying visual AI in service workflows

6. Integrating AI with CRM and Helpdesk Tools

• Linking generative AI with CRM systems (Salesforce, Zendesk, HubSpot)

• Automating case logging, customer tracking, and reporting

• Maintaining smooth workflows between AI and human agents

7. Personalisation and Customisation with AI

• Using AI to analyse customer profiles and past behaviours

• Creating dynamic, personalised support experiences

• Case examples of AI-driven personalisation success

8. Real-World Applications and Case Studies

• Industry-specific use cases (retail, finance, healthcare, tech)

• Step-by-step walkthrough of AI deployment in support roles

• Group work: solve customer queries with AI tools

9. Ethical Use of AI in Customer Support

• Understanding the risks: data privacy, bias, transparency

• Guidelines for responsible AI usage in communication

• Balancing automation with human empathy

10. Measuring AI Impact on Support Performance

• Defining KPIs and metrics for AI-enabled support teams

• Using dashboards to track AI effectiveness

• Continuous improvement using feedback loops and analytics