Course Taxonomy: Generative AI

1. Introduction to AI & Machine Learning
2. Deeper Dive into Machine Learning
3. Leveraging AI in Business & Decision Making
4. Hot Trends for AI in Business: Large Language Models (LLM), Generative AI and GPT
5. Basics of Neural Networks
6. Natural Language Processing (NLP) & Sentiment Analysis
7. Using AI for Image, Video, and Audio Processing
8. AI for Business Technical Tools: Data Science, Deep Learning & The Cloud
9. Practical Applications and the Future of AI in Business

Part 1: Expanding Your AI Vocabulary and Understanding GPT-Based Tools

• Introducing and exploring GPT-based tools
• Understanding how GPT-based tools leverage AI to provide value
• Exploring use cases of GPT-based tools in various industries
• Distinguishing between different AI models and algorithms
• Understanding the evolution and future trends of AI

Part 2:Next-Level Engagement with AI: Enhancing Communication with GPT-Based AI Tools

• Developing advanced skills to leverage GPT-based tools for improving customer communication
• Building on previous communication skills with AI tools
• Simulating customer service interactions with GPT-based tools like ChatGPT and InstructGPT
• Deepening understanding of AI capabilities in customer service
• Discussing AI-enabled customer segmentation and personalization
• Exploring AI role in handling and resolving customer complaints
• Lab: Simulating complex customer service interactions with GPT-based tools

Part 3: Streamlining Workflow with GPT-Based AI Tools

• Learning how GPT-based tools can optimize workflows and automate tasks
• Exploring tools like Trello AI, IFTTT
• Understanding the AI integration in project management tools
• Discussing the benefits of AI-driven task automation
• Lab: Setting up an AI-integrated workflow using GPT-driven apps

Part 4: Enhancing Collaboration with GPT-Based AI Tools

• Using GPT-based tools like ChatGPT to foster efficient teamwork
• Exploring AI’s role in remote work and virtual collaboration
• Understanding how AI can foster efficient teamwork
• Using AI in tools like Microsoft Teams and Google Docs
• Exploring AI’s role in remote work and virtual collaboration
• Understanding how AI can improve documentation and sharing of knowledge
• Lab: Collaborating on a project using GPT-driven suggestions and ideas

Part 5: Leveraging GPT-Based AI Tools for Smarter Decision Making

• Using GPT-based tools to support data-driven decision-making and risk management
• AI for business intelligence, beyond the basics
• Understanding AI in risk assessment, resource allocation
• Exploring the role of AI in strategic planning and decision making
• Understanding how AI can analyze trends and forecast outcomes
• Lab: Running a complex risk assessment scenario using GPT-based tools

Part 6: Boosting Personal and Team Productivity with GPT-Based AI Tools (2 hours)

• Exploring how GPT-based AI personal assistants and project management tools can enhance productivity
• Understanding AI in time management and task prioritization
• Discussing how AI can enhance personal productivity and well-being
• Exploring the role of AI in managing team productivity
• Lab: Setting up and using a GPT-based AI personal assistant

Part 7: Advanced Applications of GPT-Based AI Tools in Business

• Understanding how GPT-based tools can enhance customer experiences, marketing, HR, and sales
• Exploring advanced use cases of AI in marketing, HR, sales, and more
• Introduction to AI sentiment analysis, AI in recruiting, AI in sales forecasting
• Understanding the role of AI in enhancing customer experiences
• Discussing AI-driven personalization and predictive analytics in marketing
• Lab: Using a GPT-based tool for sentiment analysis of customer feedback

Part 8: Future-Proofing Your Business with GPT-Based AI Tools

• Identifying potential opportunities for GPT-based tool integration in your business and formulating a strategic AI plan
• Discussing the future potential of AI in business
• Identifying areas in one's own business for AI integration
• Exploring potential challenges and solutions in AI adoption
• Discussing the importance of continuous learning and adaptation in the AI landscape
• The Role of Humans in an AI-Driven World
• Lab: Developing a future AI strategy for a mock business using GPT-based tools

Bonus Topics / Addendum (can be self-paced)

• Understand essential security considerations and best practices for using AI tools at work safely and effectively.
• Permissions and access controls with AI tools
• Considerations when sharing sensitive work data with AI
• Understanding your organization's AI policies
• Safe habits when using AI tools
• Proactive steps to ensure your AI tool usage remains secure
• Ensuring data privacy when working with AI

Part 9: The Ethics and Responsibility of AI and GPT Based Tools

• Understand the ethical considerations and responsibilities of using AI in business
• Privacy and data security in AI
• Identifying and avoiding biases in AI
• Legal considerations when using AI
• Promoting responsible use of AI 

Part 1: Exploring AI in Today’s Business World

• Quick View: AI Explained & Types of AI
• Exploring AI's Role in Business
• AI in Action: Current Trends, Skills & Roles in AI
• Benefits and Risks of AI

Part 2: Meet Your New AI Friends & GPT Tools

• Get to know common user-friendly AI tools for business
• Exploring GPTs in action
• Comparing tools & Matching the right AI tool to your business needs

Part 3: The ABCs of Prompt Engineering: AI Conversations for Beginners

• Gain a foundational understanding of prompt engineering to effectively communicate with AI.
• Basics of conversational AI & The Art of Crafting Prompts
• Different Types of AI Prompts
• Quick Guide to Effective Prompt Engineering
• Refining Prompts: Making AI Work Harder for You
• Managing AI’s Strengths & Weaknesses
• Avoiding Common Pitfalls in Prompting
• Quick Tips: Guide to Writing Great Prompts

Part 4: Boosting Personal Productivity and Efficiency with AI

• Making AI Work for You
• Implementing Day to Day AI: Tools to Use
• Quick Guide for Implementing AI Intro Your Daily Workflow

Part 5: Powering Your Business with AI

• Ways AI can boost productivity and efficiency in business 
• AI-Enhanced Customer Service
• Set Up a Quick ChatBot or Virtual Assistant
• Fostering Team Productivity and Collaboration
• Project Management with AI
• Integrating AI with Business Software

Part 6: Leveraging AI for Business Intelligence and Decision Making

• AI in Business Decision-Making
• Data and AI in Business
• Business Intelligence through AI
• Market Research with AI
• Risk Assessment Powered by AI
• AI for Resource Allocation

Part 7: Staying Safe and Smart with AI Tools

• Introduction to Data Security and AI
• Keeping Data & Privacy Safe in the AI World
• Quick Look: GDPR and California CCPA
• Sharing Data: Do’s and Don’ts
• Understanding your Organization's AI Policies
• Guide to Ensure your AI Tool Usage Remains Secure
• Safe Habits when using AI Tools

Part 8: The Ethics and Responsibility of AI

• Understand the ethical considerations and responsibilities of using AI in business
• Exploring Ethical AI / Responsible AI
• Quick User’s Guide to AI Ethics
• Identifying, Avoiding and Overcoming Bias in AI Systems
• Legal considerations when using AI
• Top Ten Quick Tips for Promoting Responsible AI

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most. Topics, agenda and labs are subject to change, and may adjust during live delivery based on audience skill level, interests and participation.

1. Introduction to AI & Machine Learning
2. Deeper Dive into Machine Learning
3. Leveraging AI in Business & Decision Making
4. Hot Trends for AI in Business: Large Language Models (LLM), Generative AI and GPT
5. Basics of Neural Networks
6. Natural Language Processing (NLP) & Sentiment Analysis
   1. Lab: Use an online sentiment analysis tool to analyze customer feedback from a popular business.
7. Using AI for Image, Video, and Audio Processing
   1. Lab: Use AI in recognizing and analyzing images
8. AI for Business Technical Tools: Data Science, Deep Learning & The Cloud
   1. Tools: Azure, Google, IBM, Amazon solutions
9. Practical Applications and the Future of AI in Business

Next-Steps

• Hands-on Practice
• Resources
• AI & ML Communities

1.  Introduction to Artificial Intelligence

• Defining AI: Understanding what AI is and what it is not
• Brief history of AI: From early concepts to modern AI
• Types of AI: Narrow AI vs. General AI

2.  Foundations of Machine Learning

• What is Machine Learning?
• How ML differs from traditional programming
• Overview of ML types: Supervised, Unsupervised, and Reinforcement Learning

3.  Key Concepts and Terminologies

• Understanding algorithms, models, and data in ML
• Introduction to Neural Networks and Deep Learning
• Exploring key metrics for evaluating ML models

4. Applications of AI and ML

• Real-world applications across various industries (e.g., healthcare, finance, transportation)
• Impact of AI and ML on society and the future of work
• Ethical considerations and challenges in AI and ML

5. The AI and ML Ecosystem

• Overview of tools, languages, and platforms used in AI/ML development
• Introduction to the role of data scientists and AI/ML engineers
• Emerging trends and future directions in AI and ML

6. Getting Started with AI and ML

• Resources for further learning and exploration in AI and ML
• Pathways to becoming an AI/ML professional
• Community and networking opportunities in the AI/ML field

1. Introduction to AI APIs

• Overview of AI and its applications
• Understanding APIs in the context of AI
• Types of AI APIs (e.g., natural language processing, computer vision, machine learning models)

2. Key Players in the AI API Market

• Overview of leading AI APIs (e.g., OpenAI's GPT, Google Cloud AI, IBM Watson, Microsoft Azure AI)
• Comparative analysis of features, strengths, and limitations
• Pricing models and accessibility

3. Evaluating AI APIs for Your Needs

• Defining project requirements and objectives
• Criteria for selecting an AI API (e.g., performance, scalability, ease of integration)
• Legal and ethical considerations

4. Getting Started with AI APIs

• Setting up developer accounts and API keys
• Understanding documentation and SDKs
• Basic operations with AI APIs (e.g., sending requests, handling responses)

5. Integrating AI APIs into Applications

• Best practices for API integration
• Handling errors and exceptions
• Optimizing API usage and managing costs

6. Practical Workshop

• Guided project: Integrating an AI API into a sample application
• Troubleshooting and optimization tips

7. Q&A and course wrap-up

• The AI Revolution: 1950’s to now
• Generative AI: Real-World Applications
  • Content generation
  • Business development
  • Client delivery
  • Training
• Try Cprime’s AI Chat Bot: On your laptop, tablet, or phone
  • Chained prompts
  • Strong verbs
  • Summarize or expand responses
  • Focus for an audience or channel
  • … and much more
• What more you can do: APIs, Plugins, and Vectors – Oh my!
  • A secure AI
  • Leverage your own data
  • Import and data or website you need

1.    Custom Prompt Engineering:

• Basics of prompt engineering for specific outcomes.
• Advanced techniques for refining prompts to improve responses.
• Strategies for iterative prompt testing and optimization.
• Examples of effective prompts for various business scenarios.
• Exercise: Refining prompts in CprimeAI

2.   Advanced Gen-AI Applications:

• Introduction to advanced features and capabilities of CprimeAI.
  • (Note that other tools like ChatGPT have many of the same features. However, CprimeAI supports fully private mode – important to many organizations. Also, the free version of ChatGPT is extremely out of date.)
• Case studies of advanced applications.
• Integration with other software and platforms.
  • Multiple different modes of integration.
  • Vectorization: Pull relevant records and create a cache, e.g. recent/frequent records.
  • Direct: Pull specific records e.g. using JQL for Jira.
  • Either way, the relevant records are used as context.
  • Introduce “RAG” concept.
• Customizing responses for specific organizational needs.
  • You can type a prompt such as “Remember: if you are asked x, do y.”
  • This can be injected “under the covers” as a set of system prompts.
• Discussion: List your organization’s potential applications of Gen-AI

3.   File Manipulation:

• Techniques for automating document creation, editing, and formatting.
• Strategies for comparing and merging documents.
• Best practices for managing file storage and retrieval integration.
• Security considerations when manipulating files.
• Exercise: File manipulation in CprimeAI

4.   Data Analysis:

• Fundamentals of data querying and analysis.
• Visualizing data analysis results.
  • (Currently, LLM image generation is primitive and unreliable. However, LLMs are good at transforming output into different formats, e.g., you can output as CSV, then attach that to an Excel chart. More advanced integration is possible using commercial or open-source tools, so long as the data format can be explained to the LLM.)
• Enhancing data accuracy and reliability in reports. Introduce “do this, then think about the result of this.”
• Advanced data analysis techniques with external tools.
  • (Note: This agenda item can be customized for a specific tool, as requested for private delivery. At the very least, you can get a report from an external tool, load it, and tell the LLM to look for certain things in it. But if we know more what they want to do, we can do better.)
• Exercise: Data analysis in CprimeAI

5.   Productivity Enhancement:

• Streamlining workflow processes.
• Automating routine tasks and communications.
• Customizing for team collaboration and project management.
• Time-saving tips and tricks for daily tasks.
• Discussion: Your organization’s productivity-enhancement opportunities

6.   Integration with Internal Data Sources:

• Overview of connecting to internal databases and data lakes.
• How to ensure data privacy and security during integration.
• Techniques for real-time data analysis and insight generation.
• Case studies on successful internal data source integrations.
• Discussion: Your organization’s data that would need to be integrated with Gen-AI

7.    Business Development Applications:

• Market research and competitive analysis.
• Generating leads and identifying business opportunities.
• Crafting personalized outreach and marketing strategies.
• Analyzing customer feedback and trends.
• Exercise: Research competitors/clients using CprimeAI

8.   Limitations and Best Practices:

• Understanding the computational and contextual limitations of an LLM interface.
• Ethical considerations and responsible use.
• Troubleshooting common issues and challenges.
• Staying updated with the latest Gen-AI developments and best practices.
• Discussion: Roadmap for Gen-AI at Your organization

Part 1: Introduction

• Workshop Objectives & Attendee Pain Points
• Demystifying AI: From Myth to Reality

Part 2: Technology & Terminology Primer

• Breaking Down AI: Essential Terms and Concepts
• Linking AI to Business Operations

Part 3: Pitfalls of AI Adoption

• Common Challenges: Data Quality, Scalability, and Integration
• Addressing Ethical and Bias Concerns

Part 4: ROI Analysis for AI Initiatives

• Quantifying AI's Real-World Business Value
• Decoding the Real-World Value of AI for Your Business
• Practical Steps for Measuring AI Impact

Part 5: Map AI Solutions to Customer’s Business

• From Current Pain Points to Potential AI Solutions
• Assessing existing systems, databases, and software
• Identifying communication and integration gaps
• AI as a solution to streamline and optimize
• Strategies for Effective Data Management with AI Integration

Part 6: Conclusion

• Aligning AI with Long-Term Goals & Future Steps

Appendix: Selected AI Solutions Deep Dive

• Resources and Best Practices for Implementation