Course Taxonomy: Generative AI

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

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

Part 1: ChatGPT Basics & Underlying Concepts

We begin by spending just a little time describing ChatGPT and defining relevant terminology to set the stage for learning. The Q&A allows learners to quickly confirm their understandings so we can get to the fun and interesting part, using this tool.

1. Definitions
2. Types of GPTs (Chat & others)
3. The role of data (and your company’s data)
4. What ChatGPT is good at (and not good at)
5. Who can use ChatGPT (and for what)?
6. Q&A

Part 2: Interacting with ChatGPT

Most of our time in this short course is spent interacting with ChatGPT. Each of the sub-sections below introduces a specific usage mode, and includes:

• Examples – The instructor shows multiple examples of that usage mode.
• Play-time – Learners are allowed time to try their hand with that usage mode on their own.
• Show and Tell – Learners share and discuss their experiences with that usage mode with each other and get feedback from the instructor.

Converse with ChatGPT

We start with the most basic interactions with ChatGPT:

• Ask questions
• Use chained prompts (and break chains when needed)
• Use strong and weak verbs in prompts.
• With Examples, Play-time, Show and Tell

Prompt ChatGPT to tailor its response

ChatGPT’s responses will sometimes not meet your needs. They may be too long and verbose, too short and cryptic, or not laid out in a useful way. So, we will look at various ways to ask it to provide or restate its response in more appropriate ways.

• Summarize long responses
• Expand on short responses
• Format responses (e.g., bulleted or numbered lists, headings, etc.)
• Provide an example of content & format you want it to respond with

Prompt ChatGPT to use appropriate roles

We will look at how to focus ChatGPT’s interactions with you based on role definitions.

• Provide responses appropriate for a particular role or audience
• Provide responses appropriate to a channel (e.g. social media)
• Play a role in how it responds
• Role-play with you

Prompting ChatGPT to respond with questions

ChatGPT can do more than just answer questions. We will look at how to prompt it to ask questions as well.

• Prompt it to provide questions (e.g. for an interview)
• Prompt it to ask clarifying questions before responding

Providing data to ChatGPT

The free public version of ChatGPT cannot access data. So, we will look at ways you can provide it with the data you want it to use in its responses.

• Loading the prompt with data
• Providing data in multiple prompts for a single response
• With Examples, Play-time, Show and Tell

Part 3: Advanced Use Cases & Interactions

Connecting ChatGPT to other applications

We will explore a variety of tools that can be used to connect applications to the paid version of ChatGPT.

• Plug-ins for commercial applications (e.g. Excel) to use ChatGPT
• ChatGPT’s API for writing your own plug-ins or adding ChatGPT capabilities to your company’s applications

Making data available to ChatGPT

We will explore a variety of ways to enable ChatGPT to use other data.

• Plug-ins for ChatGPT to access external data sources (e.g. Wikipedia)
• Tools to feed large data sets into ChatGPT’s prompts
• Using a private LLM (Large Language Model) to enable ChatGPT to use specialized language and terminology
• Grounding ChatGPT on your company’s data

Part 4: Class wrap-up and Q&A