Training on: Data Annotation and AI Prompt Engineering

Session 1: Introduction to Prompt Engineering

Overview of AI Prompt Engineering:
○ Definition: Crafting effective prompts for AI models to generate desired outputs.
○ Importance: Crucial for harnessing the full potential of AI models.
○ Evolution: From simple queries to complex instructions.

Real-World Applications:
○ Case studies: Examples from various industries (e.g., customer service, content creation, research).
○ Industry examples: How prompt engineering is used in specific domains.

Introduction to ChatGPT:
○ Capabilities: Understanding and generating human-like text.
○ Usage in prompt engineering: Demonstrating how to effectively use ChatGPT for various tasks.

Effective Prompt Design:
○ Clarity: Ensuring prompts are easy to understand.
○ Context: Providing relevant background information.
○ Specificity: Being precise in your instructions.

Discussion and Q&A:
○ Interactive session: Open discussion for questions and clarifications.
○ Video Showcase: Watch a video demonstrating real-world applications of prompt engineering.

Session 2: Advanced Prompt Engineering

Recap of Last Session:
○ Review key concepts from the previous session.

Challenges in Prompt Engineering:
○ Ambiguity: Addressing unclear or vague prompts.
○ Bias: Avoiding biases in prompt design and outputs.
○ Scalability: Designing prompts that can handle large datasets.

Hands-On Experience:
○ Practice designing prompts: Participants will work on various prompt engineering exercises.

Discussion and Q&A:
○ Interactive session: Addressing questions and providing further guidance.
○ Video Showcase: Watch a video tutorial on creating effective prompts for generative AI models.


Session 3: Introduction to Data Annotation

What is Data Annotation?:
○ Definition: Labeling or tagging data for machine learning models.
○ Importance: Essential for training accurate and reliable AI models.

Why Data Annotation is Needed:
○ Role in ML, AI: How annotated data is used in machine learning and artificial intelligence.

Types of Data Annotation:
○ Text: Labeling text data for tasks like sentiment analysis or named entity recognition.
○ Image: Annotating images for object detection, image classification, or segmentation.
○ Audio: Labeling audio data for speech recognition, transcription, or sound classification.

Techniques in Data Annotation:
○ Manual: Human annotators labeling data manually.
○ Semi-automated: Combining human input with automated tools.
○ Automated: Using AI algorithms to annotate data.

Discussion and Q&A:
○ Interactive session: Discussing the different types and techniques of data annotation.
○ Video Showcase: Watch a video demonstrating the process of data annotation for a specific task.


Session 4: Practical Data Annotation

Tools and Platforms:
○ Overview of annotation tools: Introducing popular tools for data annotation (e.g., LabelImg, Prodigy).

Step-by-Step Annotation Examples:
○ Text: Demonstrating how to annotate text data for various tasks.
○ Image Annotation: Guiding participants through the process of annotating images.

Annotation Project Building:
○ Setting up and managing a project: Steps involved in creating and managing a data annotation project.

Discussion and Q&A:
○ Interactive session: Addressing questions and providing practical advice.
○ Video Showcase: Watch a tutorial on using a specific data annotation tool.


Session 5: Advanced Data Annotation

Show More Examples:
○ Real-world annotation cases: Presenting examples of data annotation projects from different industries.

Quality Assurance:
○ Ensuring annotation accuracy: Strategies for maintaining high-quality annotations.

Dealing with Ambiguous Data:
○ Strategies, examples: Approaches for handling ambiguous or unclear data.

Scaling Data Annotation Projects:
○ Crowdsourcing, automation: Methods for scaling data annotation projects efficiently.

Discussion and Q&A:
○ Final session: Wrapping up the course and addressing any remaining questions.
○ Video Showcase: Watch a video on scaling data annotation projects using crowdsourcing or automation.

Additional Activities:
Prompt Engineering Challenges: Organize a competition where participants create the most effective prompts for generative AI models.
Data Annotation Games: Create interactive games to practice data annotation tasks in a fun and engaging way.
AI-Powered Art Contest: Host a contest where students use generative AI to create original pieces of art or design.
Online Resources and Communities: Provide a list of recommended platforms for further learning and networking.

Curriculum