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AI+ Prompting Fundamentals™

AI+ Prompting Fundamentals™
  • Foundational Knowledge: Covers generative AI, ML, NLP, and neural networks essentials
  • Hands-on Learning: Offers practical training in designing and optimizing prompts
  • Industry-Relevant Skills: Prepares learners to build effective AI solutions across sectors
  • Prompting Expertise: Certifies participants to craft impactful, domain-specific prompts
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Certificate Code

AC-130

Exam Format

AI-Driven Remote Exam Proctoring

Course Overview

Important details and certification information

Instructor-led OR Self-paced course + Official exam + Digital badge
Instructor-Led: 3 Days (live or virtual)
Understand AI basics, Willingness to think creatively to generate ideas and use AI tools effectively.
50 questions, 70% passing, 90 minutes, online proctored exam

Certification Modules

  1. Course Introduction Preview

  1. 1.1 Introduction to Artificial Intelligence Preview
  2. 1.2 History of AI Preview
  3. 1.3 Machine Learning Basics Preview
  4. 1.4 Deep Learning and Neural Networks
  5. 1.5 Natural Language Processing (NLP)
  6. 1.6 Prompt Engineering Fundamentals

  1. 2.1 Introduction to the Principles of Effective PromptingPreview
  2. 2.2 Giving DirectionsPreview
  3. 2.3 Formatting ResponsesPreview
  4. 2.4 Providing Examples
  5. 2.5 Evaluating Response Quality
  6. 2.6 Dividing Labor
  7. 2.7 Applying The Five Principles
  8. 2.8 Fixing Failing Prompts

  1. 3.1 Understanding AI Tools and Models Preview
  2. 3.2 Deep Dive into ChatGPT Preview
  3. 3.3 Exploring GPT Preview
  4. 3.4 Revolutionizing Art with DALLE
  5. 3.5 Introduction to Emerging Tools using GPT
  6. 3.6 Specialized AI Models
  7. 3.7 Advanced AI Models
  8. 3.8 Google AI Innovations
  9. 3.9 Comparative Analysis of AI Tools
  10. 3.10 Practical Application Scenarios
  11. 3.11 Harnessing AI’s Potential

  1. 4.1 Zero-Shot Prompting
  2. 4.2 Few-Shot Prompting
  3. 4.3 Chain-of-Thought Prompting
  4. 4.4 Ensuring Self-Consistency in AI Responses
  5. 4.5 Generate Knowledge Prompting
  6. 4.6 Prompt Chaining
  7. 4.7 Tree of Thoughts: Exploring Multiple Solutions
  8. 4.8 Retrieval Augmented Generation
  9. 4.9 Graph Prompting and Advanced Data Interpretation
  10. 4.10 Application in Practice: Real-Life Scenarios
  11. 4.11 Practical Exercises

  1. 5.1 Introduction to Image Models
  2. 5.2 Understanding Image Generation
  3. 5.3 Style Modifiers and Quality Boosters in Image Generation
  4. 5.4 Advanced Prompt Engineering in AI Image Generation
  5. 5.5 Prompt Rewriting for Image Models
  6. 5.6 Image Modification Techniques: Inpainting and Outpainting
  7. 5.7 Realistic Image Generation
  8. 5.8 Realistic Models and Consistent Characters
  9. 5.9 Practical Application of Image Model Techniques

  1. 6.1 Introduction to Project-Based Learning in AI
  2. 6.2 Selecting a Project Theme
  3. 6.3 Project Planning and Design in AI
  4. 6.4 AI Implementation and Prompt Engineering
  5. 6.5 Integrating Text and Image Models
  6. 6.6 Evaluation and Integration in AI Projects
  7. 6.7 Engaging and Effective Project Presentation
  8. 6.8 Guided Project Example

  1. 7.1 Introduction to AI Ethics
  2. 7.2 Bias and Fairness in AI Models
  3. 7.3 Privacy and Data Security in AI
  4. 7.4 The Imperative for Transparency in AI Operations
  5. 7.5 Sustainable AI Development: An Imperative for the Future
  6. 7.6 Ethical Scenario Analysis in AI: Navigating the Complex Landscape
  7. 7.7 Navigating the Complex Landscape of AI Regulations and Governance
  8. 7.8 Navigating the Regulatory Landscape: A Guide for AI Practitioners
  9. 7.9 Ethical Frameworks and Guidelines in AI Development

  1. 1. What Are AI Agents
  2. 2. Applications and Trends of AI Agents for Prompt Engineers
  3. 3. How Does an AI Agent Work
  4. 4. Core Characteristics of AI Agents
  5. 5. Importance of AI Agents
  6. 6. Types of AI Agents

AI Tools Covered

LangChain
OpenAI's GPT
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