Course Description

Embark on an exploratory journey into the world of Generative Artificial Intelligence with this foundational course tailored for absolute beginners. Without needing any previous background in AI, start your adventure by unraveling the essence of generative AI and its operational mechanics. Engage in dynamic lessons enriched with interactive elements and practical exercises designed to cultivate your skills in crafting effective prompts and refining AI-generated content. As you progress, immerse yourself deeper into the intricacies of prominent generative AI models, exploring their distinctive features and inherent constraints. Gain hands-on experience with leading-edge platforms such as GitHub Copilot, Qdrant, and OpenAI, mastering the art of generating code and text. By the course's conclusion, you'll possess the essential knowledge and confidence to experiment with generative AI across diverse applications responsibly. This course is your gateway to understanding and leveraging the transformative potential of generative AI, setting the stage for further discovery in this rapidly advancing field.

Course curriculum

    1. Welcome to Generative AI

    2. Decoding Generative AI

    3. The Journey of AI

    4. Large Language Models at Work

    5. The Making of Large Language Models

    6. Module Recap

    7. Deep Dive into LLMs

    8. Evaluating Large Language Models

    9. Strategies for Responsible LLM Use

    10. The Pillars of AI

    11. Consolidating Insights

    12. Navigating the Ecosystem

    13. Championing Open Source

    14. Empowering AI Locally

    15. The Cloud Advantage

    16. Bringing It All Together

    17. Next Steps in Generative AI

    1. The Essence of Prompt Engineering

    2. Prompting Varieties

    3. Crafting Contextual Prompts

    4. Enhancing Prompts with Examples

    5. Summarizing Prompt Engineering

    6. Setting the Tone and Persona

    7. Refining Context with Iterative Feedback

    8. Navigating LLM Limitations

    9. In-depth Review of Prompt Techniques

    10. Understanding LLM Constraints

    11. Task Decomposition for Effective Outcomes

    12. Advanced Prompting Techniques

    13. Prompt Engineering Mastery

    14. Prompt Design for Specific Outcomes

    15. Iterative Development and Feedback Loops

    16. Customizing Tone and Style

    17. Overcoming Common Prompting Challenges

    18. Module Closure

    1. Module Introduction

    2. Generative AI Applications

    3. API Application Insights

    4. Embedded Model Insights

    5. Multi-Model Applications

    6. AI Application Challenges & Highlights

    7. Module Summary

    8. RAG Fundamentals

    9. Data Management for RAG

    10. Optimizing Embeddings & Search

    11. RAG in LLM Applications

    12. RAG Module Summary

    13. Deployment Introduction

    14. Application Deployment Insights

    15. Azure Deployment Basics

    16. Configuring Azure Components

    17. Cloud Deployment with Azure

    18. Deployment Module Summary

    19. Final Recap

    20. Closing Remarks

About this course

  • $9.99
  • 55 lessons
  • 3.5 hours of video content

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