Prompt Engineering Training

• Mastering AI Prompt Engineering: A Practical Course for Professionals Across Africa's Economy
• Trainers Guide
• Developed by Dr David King Boison, CEO of Knowledge Web Centre
• Sponsored by ADCB/VTC
• AI Economy


  The economic environment where AI technologies play a pivotal role in driving innovation, efficiency, and economic growth
• Drivers of the AI Economy


  • Advancements in computing power
  • Availability of big data
  • Breakthroughs in machine learning and deep learning technologies
• Impact of AI on Healthcare


  • Diagnosis and treatment
  • Drug discovery
• Impact of AI on Finance


  • Algorithmic trading
  • Fraud detection
• Impact of AI on Manufacturing


  • Predictive maintenance
  • Supply chain optimization
• Impact of AI on Agriculture

  • Precision farming
• Productivity Growth


  AI can automate routine tasks, allowing human workers to focus on higher-value activities
• Economic Benefits and Challenges of the AI Economy


  • Productivity growth
  • Job creation vs. displacement
  • Ethical and governance challenges
• The landscape of Artificial Intelligence is rich with a variety of tools designed to cater to different needs
• Categories of AI Tools


  • Machine learning platforms
  • Deep learning libraries
  • Natural language processing (NLP) tools
  • AI development platforms
  • Specialized AI applications for sectors
• Machine Learning Platforms


  • Scikit-learn
  • TensorFlow
  • PyTorch
• Natural Language Processing (NLP) Tools


  • NLTK (Natural Language Toolkit)
  • GPT (Generative Pre-trained Transformer)
• AI Development Platforms


  • Google AI Platform
  • Microsoft Azure AI
• Specialized AI Applications


  • Healthcare (DeepMind Health)
  • Finance (Kensho)
• The rapid advancement in AI tools and technologies emphasizes the importance of staying informed about the latest developments
• The increased accessibility of AI tools to a wider audience raises the need for discussions on ethical use
• Classify sentiments


  1. Data Collection

  2. Data Preprocessing

  3. Feature Extraction

  4. Model Training

  5. Evaluation and Deployment
• Sentiment analysis involves classifying sentiments as positive, negative, or neutral
• Data Collection


  • Gather a dataset of customer reviews from a platform like Yelp or Amazon
• Data Preprocessing


  • Use NLTK to clean and preprocess the text data (tokenization, removing stopwords, stemming)
• Feature Extraction


  • Convert text data into a form that can be used by machine learning algorithms (e.g., TF-IDF vectorization)
• Model Training


  • Train a sentiment analysis model using a machine learning algorithm (e.g., logistic regression, support vector machine) with scikit-learn
• Evaluation and Deployment


  • Evaluate the model's performance on a test dataset and deploy the model for real-time sentiment analysis
• The Evolving Landscape: Highlight the rapid advancement in AI tools and technologies, emphasizing the importance of staying informed about the latest developments
• Accessibility and Ethical Use: Discuss the increased accessibility of AI tools to a wider audience, including non-experts, and the importance of ethical considerations in AI development and deployment
• Encourage participants to engage in hands-on projects using AI tools to solve real-world problems. This practical experience is invaluable for understanding the capabilities and limitations of current AI technologies
• Recommend resources for further learning, such as online courses on AI tool-specific platforms (e.g., Coursera, Udacity), GitHub repositories for open-source projects, and AI community forums (e.g., Reddit's r/MachineLearning, Stack Overflow)
• Suggest joining hackathons or AI competitions (e.g., Kaggle competitions) as a way to apply skills in a competitive and collaborative environment, offering real-world problems to solve
• Clear Problem Definition


  • Start with a well-defined problem statement. Successful AI projects are those that address specific, well-articulated problems. This involves understanding the problem's context, its importance, and how AI can provide a viable solution
• Data Quality and Availability


  • The foundation of any AI project is data. Ensuring access to high-quality, relevant data sets is crucial. This includes practices for data collection, cleaning, annotation, and ensuring data privacy and security
• Choosing the Right Tools and Technologies


  • Selecting appropriate AI tools and technologies that align with the project goals and the problem's complexity is essential. This decision should be based on the project's specific needs, the team's expertise, and the scalability and maintainability of the AI solution
• Iterative Development and Testing


  • AI projects benefit from an iterative approach, where solutions are developed, tested, and refined in cycles. This allows for continuous improvement and adaptation to new insights or changes in project requirements
• Ethical Considerations and Bias Mitigation


  • Incorporating ethical guidelines and practices to identify, mitigate, and manage biases in AI models. This also involves ensuring that AI applications respect privacy and are transparent and fair
• Healthcare Diagnosis


  • An AI system developed to diagnose diseases from medical images, such as X-rays or MRIs. The success factors included high-quality, annotated medical images, an iterative approach to model training and testing, and a clear focus on augmenting rather than replacing human expertise
• Customer Service Chatbots


  • A retail company's deployment of an AI-powered chatbot to handle customer inquiries. Key to its success was the continuous updating of the chatbot's knowledge base, regular monitoring and tuning to improve accuracy, and an initial clear definition of the chatbot's scope
• Steps to Implement Effective AI Strategies


  1. Define and Understand the AI Project's Scope

  2. Evaluate and Prepare the Data

  3. Select Appropriate AI Technologies

  4. Develop a Prototype and Test Iteratively

  5. Incorporate Ethics from the Start