Bespoke - Artificial Intelligence Training

Practical Sessions

In-Orbit Servicing and Manufacturing Team:

Session 1: GenAI for Business Processes and Automation

Objective: Introduce the team to how Generative AI can streamline business processes, automate routine tasks, and enhance productivity.

Agenda:

1. Introduction to GenAI (15 minutes)

• Overview of Generative AI and its capabilities.
• Examples of GenAI applications in business.

2. Automating Routine Business Tasks (30 minutes)

• Using AI to schedule meetings, manage emails, and handle customer inquiries.
• Practical exercise: Automate email responses using an AI tool (e.g., Gmail's Smart Reply).

3. Document Generation and Summarization (30 minutes)

• Creating reports, summaries, and business documents using AI.
• Practical exercise: Generate a business report summary using an AI tool (e.g., OpenAI's GPT-3).

4. AI for Data Analysis and Insights (30 minutes)

• Analyzing business data to extract insights using AI.
• Practical exercise: Use an AI tool (e.g., Tableau with GPT-3 integration) to analyze a dataset and generate insights.

5. Q&A and Discussion (15 minutes)

• Addressing any questions and discussing potential applications in the team’s workflow.

Materials Needed:

• Laptops with internet access.
• Access to AI tools (e.g., Gmail, OpenAI GPT-3, Tableau).

Session 2: GenAI for Coding and Software Development

Objective: Enable the team to leverage Generative AI for enhancing coding productivity, debugging, and software development.

Agenda:

1. Introduction to GenAI in Coding (15 minutes)

• Overview of how AI can assist in coding and software development.
• Examples of AI tools for coding.

2. Code Generation and Completion (30 minutes)

• Using AI to generate and autocomplete code.
• Practical exercise: Use an AI coding assistant (e.g., GitHub Copilot) to write a simple script.

3. Debugging and Code Optimization (30 minutes)

• Leveraging AI to identify and fix bugs, and optimize code.
• Practical exercise: Use an AI tool (e.g., DeepCode) to debug and optimize a given code snippet.

4. AI for Code Documentation and Comments (30 minutes)

• Generating documentation and comments using AI.
• Practical exercise: Use an AI tool (e.g., KDocs) to automatically document a piece of code.

5. Q&A and Discussion (15 minutes)

• Addressing questions and discussing how AI can be integrated into the team's coding practices.

Materials Needed:

• Laptops with internet access.
• Access to AI coding tools (e.g., GitHub Copilot, DeepCode, KDocs).

Session 3: GenAI for Presentation Preparation and Image Generation

Objective:Teach the team how to use Generative AI for creating impactful presentations and generating relevant images.

Agenda:

1. Introduction to GenAI for Creative Tasks (15 minutes)

• Overview of AI tools for presentations and image generation.
• Examples of AI-generated content.

2. AI for Presentation Creation (30 minutes)

• Using AI to create slides, content, and designs.
• Practical exercise: Use an AI tool (e.g., Beautiful.ai) to create a presentation on a chosen topic.

3. AI for Image Generation and Editing (30 minutes)

• Generating and editing images using AI.
• Practical exercise: Use an AI image generator (e.g., DALL-E, MidJourney) to create images for the presentation.

4. Enhancing Presentations with AI (30 minutes)

• Integrating AI-generated content into presentations for enhanced impact.
• Practical exercise: Combine AI-generated slides and images into a cohesive presentation using PowerPoint or Google Slides.

5. Q&A and Discussion (15 minutes)

• Addressing questions and discussing how AI can improve the team's presentation and image creation processes.

Materials Needed:

• Laptops with internet access.
• Access to AI tools (e.g., Beautiful.ai, DALL-E, PowerPoint, Google Slides).

General Preparation for All Sessions:

1. Pre-Session Preparation:

• Ensure all participants have necessary accounts and access to required AI tools.
• Provide a brief pre-reading material or tutorial videos on the tools to be used.

2. During the Session:

• Ensure a hands-on, interactive approach where participants can follow along with practical exercises.
• Provide step-by-step guidance and support during exercises.

3. Post-Session Follow-Up:

• Share additional resources and tutorials for further learning.
• Collect feedback to understand the effectiveness of the training and areas for improvement.

User Centred Design Team:

Session 1: AI for Summarizing Interviews

Objective: Enable the team to leverage AI for summarizing user interviews while addressing concerns over AI bias and ensuring the summaries are complemented by designers’ notes.

Agenda:

1. Introduction to AI in User Research (15 minutes)

• Overview of how AI can assist in summarizing interviews.
• Discussing AI bias and its implications.

2. Introduction to AI Summarization Tools (15 minutes)

• Overview of various AI tools for summarization (e.g., Otter.ai, Descript, Trint).
• Demonstration of a selected tool.

3. Hands-On Exercise: AI-Generated Summaries (30 minutes)

• Participants use an AI tool to generate summaries from a provided interview transcript.
• Compare AI-generated summaries with designers’ notes.

4. Addressing AI Bias (30 minutes)

• Techniques to identify and mitigate AI bias in summaries.
• Practical exercise: Review AI summaries for potential biases and cross-reference with designers’ notes to ensure accuracy and context.

5. Q&A and Discussion (30 minutes)

• Addressing questions and discussing best practices for using AI alongside human notes to ensure comprehensive and unbiased summaries.

Materials Needed:

• Laptops with internet access.
• Access to AI summarization tools (e.g., Otter.ai, Descript, Trint).
• Sample interview transcripts.

Session 2: Creating Meeting Minutes with AI

Objective: Teach the team how to use AI to efficiently create accurate minutes from meetings.

Agenda:

1. Introduction to AI for Meeting Minutes (15 minutes)

• Overview of AI capabilities in generating meeting minutes.
• Benefits of using AI for minute-taking.

2. Introduction to AI Minute-Taking Tools (15 minutes)

• Overview of various AI tools for creating meeting minutes (e.g., Fireflies.ai, Microsoft Teams, Zoom transcription).
• Demonstration of a selected tool.

3. Hands-On Exercise: Generating Meeting Minutes (30 minutes)

• Participants use an AI tool to create minutes from a provided meeting recording or transcript.
• Editing and refining AI-generated minutes to ensure completeness and accuracy.

4. Ensuring Accuracy and Context (30 minutes)

• Techniques to review and enhance AI-generated minutes.
• Practical exercise: Cross-checking AI-generated minutes with actual meeting notes and recordings.

5. Q&A and Discussion (30 minutes)

• Addressing questions and discussing strategies for integrating AI-generated minutes into the team’s workflow.

Materials Needed:

• Laptops with internet access.
• Access to AI minute-taking tools (e.g., Fireflies.ai, Microsoft Teams, Zoom transcription).
• Sample meeting recordings or transcripts.

Session 3: Integrating AI into the Design Process

Objective: Equip the team with strategies to effectively integrate AI tools into their design process, ensuring enhanced productivity while maintaining user-centricity.

Agenda:

1. Recap and Integration Overview (15 minutes)

• Quick recap of key points from the previous sessions.
• Overview of integrating AI tools into the design workflow.

2. Practical Exercise: Combining AI Tools for Comprehensive Output (30 minutes)

• Participants combine AI tools for summarizing interviews and generating meeting minutes.
• Practical exercise: Using AI to summarize a user interview and create minutes from a follow-up design meeting.

3. Review and Feedback Mechanism (30 minutes)

• Techniques for regularly reviewing AI outputs to ensure they meet quality standards.
• Practical exercise: Set up a feedback loop for continuous improvement of AI-assisted tasks.

4. Customizing AI Tools for Team Needs (30 minutes)

• Exploring customization options in AI tools to better fit the team’s specific requirements.
• Practical exercise: Customize an AI tool’s settings and templates to align with the team’s workflow.

5. Q&A and Future Directions (15 minutes)

• Addressing final questions and discussing the future potential of AI in user-centered design.
• Exploring additional AI tools and resources for further learning.

Materials Needed:

• Laptops with internet access.
• Access to AI tools used in previous sessions.
• Sample data for practical exercises.

General Preparation for All Sessions:

1. Pre-Session Preparation:

• Ensure all participants have necessary accounts and access to required AI tools.
• Provide a brief pre-reading material or tutorial videos on the tools to be used.

2. During the Session:

• Ensure a hands-on, interactive approach where participants can follow along with practical exercises.
• Provide step-by-step guidance and support during exercises.

3. Post-Session Follow-Up:

• Share additional resources and tutorials for further learning.
• Collect feedback to understand the effectiveness of the training and areas for improvement.

Ubicuous Connectivity Team:

Session 1: AI for Content Generation

Objective: Equip the team with skills to use AI for generating high-quality content relevant to the telecom industry.

Agenda:

1. Introduction to AI for Content Generation (15 minutes)

• Overview of AI capabilities in content creation.
• Examples of AI-generated content in the telecom industry.

2. AI Tools for Content Generation (15 minutes)

• Introduction to various AI content generation tools (e.g., OpenAI GPT-3, Jasper, Copy.ai).
• Demonstration of a selected tool.

3. Hands-On Exercise: Generating Technical Articles (30 minutes)

• Practical exercise: Use an AI tool to generate a technical article on a telecom-related topic.
• Editing and refining AI-generated content for accuracy and relevance.

4. Creating Marketing Content with AI (30 minutes)

• Using AI to generate marketing materials such as blog posts, social media content, and newsletters.
• Practical exercise: Create a marketing blog post or social media content using an AI tool.

5. Q&A and Discussion (30 minutes)

• Addressing questions and discussing best practices for using AI in content generation.
• Exploring how AI-generated content can be integrated into the team's workflow.

Materials Needed:

• Laptops with internet access.
• Access to AI content generation tools (e.g., OpenAI GPT-3, Jasper, Copy.ai).
• Sample topics for content generation.

Session 2: AI & Telecoms

Objective: Teach the team how AI can be applied within the telecom industry to improve operations and customer experiences.

Agenda:

1. Introduction to AI in Telecoms (15 minutes)

• Overview of AI applications in the telecom industry.
• Case studies of AI implementations in telecoms.

2. Network Optimization with AI (30 minutes)

• Using AI for network planning, optimization, and maintenance.
• Practical exercise: Simulate network optimization using an AI tool.

3. Customer Service Enhancements with AI (30 minutes)

• AI-driven customer service solutions like chatbots and virtual assistants.
• Practical exercise: Set up and configure a simple AI chatbot for handling customer queries.

4. AI for Predictive Maintenance (30 minutes)

• Leveraging AI for predictive maintenance of telecom infrastructure.
• Practical exercise: Use an AI tool to analyze data and predict maintenance needs.

5. Q&A and Discussion (15 minutes)

• Addressing questions and discussing the potential and challenges of AI in telecoms.

Materials Needed:

• Laptops with internet access.
• Access to AI tools for network optimization and predictive maintenance.
• Sample data for exercises.

Session 3: AI and Edge Computing for Telecoms

Objective: Introduce the team to the integration of AI with edge computing in telecoms and provide training on practical applications.

Agenda:

1. Introduction to AI and Edge Computing (15 minutes)

• Overview of edge computing and its relevance to AI and telecoms.
• Examples of AI applications at the edge.

2. AI-Driven Edge Computing Solutions (30 minutes)

• How AI can enhance edge computing in telecoms for real-time data processing and decision-making.
• Practical exercise: Deploy a simple AI model on an edge device.

3. Edge AI for IoT and Smart Devices (30 minutes)

• Applications of AI at the edge for IoT and smart devices in telecom networks.
• Practical exercise: Implement a basic AI solution for an IoT use case.

4. Internal Training on AI and Edge Computing (30 minutes)

• Developing internal training materials to educate the team on AI and edge computing.
• Practical exercise: Create a training module or presentation using AI tools.

5. Q&A and Discussion (15 minutes)

• Addressing questions and discussing the future trends and opportunities in AI and edge computing for telecoms.

Materials Needed:

• Laptops with internet access.
• Access to edge computing platforms and AI deployment tools.
• Sample edge devices and IoT setups.

General Preparation for All Sessions:

1. Pre-Session Preparation:

• Ensure all participants have necessary accounts and access to required AI tools.
• Provide a brief pre-reading material or tutorial videos on the tools to be used.

2. During the Session:

• Ensure a hands-on, interactive approach where participants can follow along with practical exercises.
• Provide step-by-step guidance and support during exercises.

3. Post-Session Follow-Up:

• Share additional resources and tutorials for further learning.
• Collect feedback to understand the effectiveness of the training and areas for improvement.

Facilities Team:

Session 1: AI for Generating Draft Policies and Procedures

Objective: Teach the team how to use AI for creating draft policies and procedures to streamline documentation processes.

Agenda:

1. Introduction to AI for Documentation (15 minutes)

• Overview of AI capabilities in generating documentation.
• Examples of AI-generated policies and procedures.

2. AI Tools for Policy Generation (15 minutes)

• Introduction to various AI documentation tools (e.g., OpenAI GPT-3, Jasper).
• Demonstration of a selected tool.

3. Hands-On Exercise: Generating Draft Policies (30 minutes)

• Practical exercise: Use an AI tool to generate a draft policy for a facility management scenario (e.g., safety protocols, maintenance schedules).
• Review and edit the AI-generated policy to ensure accuracy and relevance.

4. Customizing Procedures with AI (30 minutes)

• Using AI to create and customize standard operating procedures (SOPs) for specific facility tasks.
• Practical exercise: Generate and tailor an SOP for a specific facility management task using an AI tool.

5. Q&A and Discussion (30 minutes)

• Addressing questions and discussing best practices for using AI in policy and procedure generation.
• Exploring how AI-generated policies can be integrated into the team’s workflow.

Materials Needed:

• Laptops with internet access.
• Access to AI documentation tools (e.g., OpenAI GPT-3, Jasper).
• Sample topics for policy and procedure generation.

Session 2: AI for Checking Code

Objective: Enable the team to leverage AI tools for checking and validating code used in facilities management software and automation systems.

Agenda:

1. Introduction to AI in Code Checking (15 minutes)

• Overview of AI capabilities in code checking and validation.
• Examples of AI tools used for code analysis.

2. AI Tools for Code Checking (15 minutes)

• Introduction to various AI code checking tools (e.g., GitHub Copilot, DeepCode, SonarQube).
• Demonstration of a selected tool.

3. Hands-On Exercise: Checking Sample Code (30 minutes)

• Practical exercise: Use an AI tool to check a sample code snippet for errors and best practices.
• Review the AI-generated feedback and make necessary corrections.

4. Enhancing Code Quality with AI (30 minutes)

• Techniques to improve code quality using AI recommendations.
• Practical exercise: Refactor a piece of code based on AI suggestions for optimization and readability.

5. Q&A and Discussion (30 minutes)

• Addressing questions and discussing how AI can assist in maintaining high code quality in facility management software.

Materials Needed:

• Laptops with internet access.
• Access to AI code checking tools (e.g., GitHub Copilot, DeepCode, SonarQube).
• Sample code snippets for exercises.

Session 3: Integrating AI into Facilities Management Workflow

Objective: Provide comprehensive training on integrating AI tools into the facilities management workflow for enhanced efficiency and productivity.

Agenda:

1. Recap and Integration Overview (15 minutes)

• Quick recap of key points from the previous sessions.
• Overview of integrating AI tools into the facilities management workflow.

2. Practical Exercise: Combining AI Tools (30 minutes)

• Participants combine AI tools for policy generation and code checking.
• Practical exercise: Use AI to generate a draft policy and check associated code for an integrated task (e.g., implementing a new maintenance procedure that involves coding automation scripts).

3. Review and Feedback Mechanism (30 minutes)

• Techniques for regularly reviewing AI outputs to ensure they meet quality standards.
• Practical exercise: Set up a feedback loop for continuous improvement of AI-assisted tasks in policy generation and code checking.

4. Customizing AI Tools for Team Needs (30 minutes)

• Exploring customization options in AI tools to better fit the team’s specific requirements.
• Practical exercise: Customize an AI tool’s settings and templates to align with the team’s workflow in policy generation and code checking.

5. Q&A and Future Directions (15 minutes)

• Addressing final questions and discussing the future potential of AI in facilities management.
• Exploring additional AI tools and resources for further learning.

Materials Needed:

• Laptops with internet access.
• Access to AI tools used in previous sessions.
• Sample data and scenarios for practical exercises.

General Preparation for All Sessions:

1. Pre-Session Preparation:

• Ensure all participants have necessary accounts and access to required AI tools.
• Provide a brief pre-reading material or tutorial videos on the tools to be used.

2. During the Session:

• Ensure a hands-on, interactive approach where participants can follow along with practical exercises.
• Provide step-by-step guidance and support during exercises.

3. Post-Session Follow-Up:

• Share additional resources and tutorials for further learning.
• Collect feedback to understand the effectiveness of the training and areas for improvement.

Future Systems Team:

Session 1: AI for Training and Signal Analysis

Objective: Equip the team with skills to use AI for training models and analyzing signals in future systems applications.

Agenda:

1. Introduction to AI for Signal Analysis (15 minutes)

• Overview of AI capabilities in signal processing and analysis.
• Examples of AI applications in signal analysis.

2. AI Tools for Signal Analysis (15 minutes)

• Introduction to various AI tools for signal processing (e.g., MATLAB, Python libraries like SciPy and NumPy, TensorFlow).
• Demonstration of a selected tool.

3. Hands-On Exercise: Signal Data Preprocessing (30 minutes)

• Practical exercise: Use an AI tool to preprocess and clean a signal dataset.
• Techniques for noise reduction and signal enhancement.

4. Training AI Models for Signal Analysis (30 minutes)

• Using AI to train models for specific signal analysis tasks (e.g., classification, anomaly detection).
• Practical exercise: Train a neural network to classify signals using TensorFlow or PyTorch.

5. Q&A and Discussion (30 minutes)

• Addressing questions and discussing best practices for using AI in signal analysis.
• Exploring potential applications in the team's projects.

Materials Needed:

• Laptops with internet access.
• Access to AI tools for signal processing (e.g., MATLAB, TensorFlow, PyTorch).
• Sample signal datasets.

Session 2: AI for Testing and Augmentation

Objective: Teach the team how to leverage AI for testing system functionalities and augmenting data for improved model training.

Agenda:

1. Introduction to AI for Testing and Augmentation (15 minutes)

• Overview of AI applications in system testing and data augmentation.
• Benefits of using AI for testing and augmentation.

2. AI Tools for Testing (15 minutes)

• Introduction to AI tools for automated testing (e.g., Selenium, Test.ai).
• Demonstration of a selected tool.

3. Hands-On Exercise: Automated Testing (30 minutes)

• Practical exercise: Use an AI tool to create and run automated tests for a system or application.
• Analyzing test results and identifying issues.

4. Data Augmentation Techniques (30 minutes)

• Using AI to generate synthetic data and augment existing datasets.
• Practical exercise: Apply data augmentation techniques to a dataset using Python libraries (e.g., Augmentor, Albumentations).

5. Q&A and Discussion (30 minutes)

• Addressing questions and discussing the integration of AI in testing and data augmentation.
• Exploring how augmented data can improve model performance.

Materials Needed:

• Laptops with internet access.
• Access to AI testing tools (e.g., Selenium, Test.ai).
• Sample data for augmentation exercises.

Session 3: AI Bridge Sensor Fusion

Objective: Introduce the team to AI techniques for sensor fusion, particularly for bridging and integrating data from multiple sensors.

Agenda:

1. Introduction to Sensor Fusion (15 minutes)

• Overview of sensor fusion and its importance in AI applications.
• Examples of sensor fusion in future systems.

2. AI Techniques for Sensor Fusion (15 minutes)

• Introduction to AI algorithms for sensor fusion (e.g., Kalman filters, neural networks).
• Demonstration of a selected algorithm.

3. Hands-On Exercise: Implementing Sensor Fusion (30 minutes)

• Practical exercise: Use AI to combine data from multiple sensors and improve decision-making.
• Implementing a Kalman filter or a neural network-based sensor fusion model.

4. Case Study: Sensor Fusion in Action (30 minutes)

• Review of a case study where sensor fusion was successfully applied.
• Practical exercise: Analyze and interpret the results of the sensor fusion model.

5. Q&A and Future Directions (30 minutes)

• Addressing questions and discussing future trends in AI sensor fusion.
• Exploring additional AI tools and resources for further learning.

Materials Needed:

• Laptops with internet access.
• Access to AI tools and libraries for sensor fusion (e.g., Python libraries, MATLAB).
• Sample sensor data for practical exercises.

General Preparation for All Sessions:

1. Pre-Session Preparation:

• Ensure all participants have necessary accounts and access to required AI tools.
• Provide a brief pre-reading material or tutorial videos on the tools to be used.

2. During the Session:

• Ensure a hands-on, interactive approach where participants can follow along with practical exercises.

• Provide step-by-step guidance and support during exercises.

3. Post-Session Follow-Up:

• Share additional resources and tutorials for further learning.
• Collect feedback to understand the effectiveness of the training and areas for improvement.

Geospatial Intelligence Team:

Session 1: Computer Vision, Classification, and Analysis

Objective:Enable the team to utilize AI for computer vision tasks, including image classification and analysis, with a focus on geospatial data.

Agenda:

1. Introduction to Computer Vision (15 minutes)

• Overview of computer vision and its applications in geospatial intelligence.
• Examples of computer vision tasks in geospatial data analysis.

2. AI Tools for Computer Vision (15 minutes)

• Introduction to computer vision tools and frameworks (e.g., OpenCV, TensorFlow, PyTorch).
• Demonstration of a selected tool.

3. Hands-On Exercise: Image Classification (30 minutes)

• Practical exercise: Use an AI tool to classify geospatial images (e.g., satellite images, aerial photos).
• Training a simple CNN model for image classification using TensorFlow or PyTorch.

4. Geospatial Image Analysis (30 minutes)

• Techniques for analyzing geospatial images using AI.
• Practical exercise: Perform object detection and segmentation on geospatial images.

5. Q&A and Discussion (30 minutes)

• Addressing questions and discussing best practices for computer vision in geospatial data analysis.
• Exploring potential applications in the team's projects.

Materials Needed:

• Laptops with internet access.
• Access to computer vision tools and frameworks (e.g., OpenCV, TensorFlow, PyTorch).
• Sample geospatial images for exercises.

Session 2: Predictive Analytics

Objective: Teach the team how to apply AI for predictive analytics in geospatial intelligence, using machine learning models to make predictions based on geospatial data.

Agenda:

1. Introduction to Predictive Analytics (15 minutes)

• Overview of predictive analytics and its importance in geospatial intelligence.
• Examples of predictive models in geospatial applications.

2. AI Tools for Predictive Analytics (15 minutes)

• Introduction to predictive analytics tools (e.g., scikit-learn, XGBoost, TensorFlow).
• Demonstration of a selected tool.

3. Hands-On Exercise: Building Predictive Models (30 minutes)

• Practical exercise: Use an AI tool to build a predictive model using geospatial data (e.g., predicting land use changes, weather patterns).
• Training a regression or classification model using scikit-learn.

4. Evaluating Model Performance (30 minutes)

• Techniques for evaluating and improving model performance.
• Practical exercise: Evaluate the predictive model using metrics like accuracy, precision, and recall.

5. Q&A and Discussion (30 minutes)

• Addressing questions and discussing best practices for predictive analytics in geospatial intelligence.
• Exploring potential use cases and applications in the team's projects.

Materials Needed:

• Laptops with internet access.
• Access to predictive analytics tools (e.g., scikit-learn, TensorFlow).
• Sample geospatial datasets for exercises.

Session 3: Code Development, Checking, and Generalized ML and LLMs Applied to Geospatial Data

Objective: Equip the team with skills to develop and check code for geospatial intelligence applications, and apply generalized ML and LLMs to geospatial data.

Agenda:

1. Introduction to Code Development and Checking (15 minutes)

• Overview of best practices in code development and checking.
• Importance of code quality in geospatial intelligence projects.

2. AI Tools for Code Checking (15 minutes)

• Introduction to AI tools for code checking and development (e.g., GitHub Copilot, DeepCode, SonarQube).
• Demonstration of a selected tool.

3. Hands-On Exercise: Developing and Checking Code (30 minutes)

• Practical exercise: Use an AI tool to develop and check code for a geospatial application (e.g., a script for data preprocessing or analysis).
• Review and refine the code based on AI feedback.

4. Applying Generalized ML and LLMs to Geospatial Data (30 minutes)

• Introduction to generalized machine learning models and large language models (LLMs) in geospatial data.
• Practical exercise: Use a pre-trained LLM (e.g., GPT-3) to analyze and extract insights from geospatial data.

5. Q&A and Future Directions (30 minutes)

• Addressing questions and discussing the future potential of AI in geospatial intelligence.
• Exploring additional AI tools and resources for further learning.

Materials Needed:

• Laptops with internet access.
• Access to AI tools for code development and checking (e.g., GitHub Copilot, DeepCode).
• Access to LLMs (e.g., GPT-3 via OpenAI API).
• Sample code and geospatial datasets for exercises.

General Preparation for All Sessions:

1. Pre-Session Preparation:

• Ensure all participants have necessary accounts and access to required AI tools.
• Provide a brief pre-reading material or tutorial videos on the tools to be used.

2. During the Session:

• Ensure a hands-on, interactive approach where participants can follow along with practical exercises.
• Provide step-by-step guidance and support during exercises.

3. Post-Session Follow-Up:

• Share additional resources and tutorials for further learning.
• Collect feedback to understand the effectiveness of the training and areas for improvement.