Prompting AI tools¶

What is generative AI and its applications?
Understanding language models and their training process
Introduction to ChatGPT and its capabilities

Strengths and weaknesses of ChatGPT
Common challenges in generating desired responses
Examples:
    Prompt: "Tell me a joke."
        Expected Output: "Why don't scientists trust atoms? Because they make up everything!"
        Unexpected Output: "I'm sorry, I can't generate jokes."

Role of prompts in guiding AI-generated responses
Impact of prompt formulation on output quality
Key considerations for effective prompts
Tips:
    Be clear and concise in your instructions.
    Provide specific details and context to guide the AI's understanding.
    Use prompts as questions or statements to elicit desired responses.

Articulating clear instructions for the AI model
Avoiding ambiguity and providing necessary context
Examples:
    Prompt: "Translate the following English sentence to French: 'Hello, how are you?'"
        Expected Output: "Bonjour, comment ça va ?"
        Unexpected Output: "I am an AI model trained on English language only."

Leveraging preceding conversation context effectively
Specifying requirements and desired outcomes explicitly
Examples:
    Prompt: "Based on our previous discussion, what are some steps I can take to improve my programming skills?"
        Expected Output: "You can start by practicing coding exercises regularly and building small projects to apply your knowledge."
        Unexpected Output: "Learning programming can be challenging, but it's important to stay motivated and ask for help when needed."

Open-ended prompts for creative outputs
Multiple-choice prompts for focused responses
Conditional and task-oriented prompts for specific tasks
Examples:
    Prompt: "Write a short story about a magical adventure."
        Output: AI generates a creative story based on the prompt.
    Prompt: "Which of the following programming languages is statically typed? A) Python B) JavaScript C) Java"
        Output: AI selects the correct option (C) based on the given choices.

Setting ethical boundaries for AI-generated content
Balancing creative freedom and responsible generation
Tips:
    Avoid generating harmful or inappropriate content.
    Guide the AI to provide informative and helpful responses.
    Ensure outputs adhere to ethical guidelines and community standards.

Adjusting temperature parameter for output randomness
Utilizing top-k and top-p sampling methods
Examples:
    Prompt: "Tell me about the benefits of exercise."
        Output (Temperature: 0.2): "Regular exercise has numerous health benefits such as improved cardiovascular health and increased energy levels."
        Output (Temperature: 0.8): "Exercise can improve your overall well-being and make you feel amazing! It helps to keep your heart healthy and boosts your mood."

Utilizing system messages to guide the AI's behavior
Incorporating user messages for interactive conversations
Examples:
    Prompt: User: "Can you recommend a good restaurant in New York City?"
        Output: AI: "Sure! Based on customer reviews, I recommend trying 'The Spicy Spoon' in Manhattan. They offer a diverse menu and excellent service."
    Prompt: User: "What are the symptoms of COVID-19?"
        Output: AI: "The common symptoms of COVID-19 include fever, cough, and difficulty breathing. If you experience these symptoms, it's important to seek medical advice."

Understanding bias in language models
Techniques to identify and mitigate bias in outputs
Tips:
    Review and validate the generated content for potential biases.
    Provide diverse and inclusive prompts to promote fairness.
    Continuously refine the prompts to minimize biased responses.

Using conditioning techniques for specific outputs
Crafting prompts for task-oriented responses
Examples:
    Prompt: "Write a Python code snippet to calculate the factorial of a given number."
        Output: AI generates a Python code snippet to calculate factorial.
    Prompt: "What are some good books to learn web development?"
        Output: AI generates a list of recommended books for learning web development.

Assessing the quality and coherence of outputs
Techniques for evaluating accuracy and relevance
Tips:
    Evaluate outputs for correctness, coherence, and alignment with the prompt.
    Provide feedback and iterate on the prompts to improve the AI's performance.

Problem-Solving Prompts:
Example: "Write a Python function that checks if a string is a palindrome."

Code Review Prompts:
Example: "Please review this code snippet and suggest any improvements or optimizations:"

Technical Explanation Prompts:
Example: "Explain the concept of object-oriented programming and provide an example in a programming language of your choice."

Best Practice Prompts:
Example: "What are some recommended practices for securing user authentication in web applications?"

Algorithmic Prompts:
Example: "Design an algorithm to find the shortest path between two nodes in a graph."

Framework or Library Usage Prompts:
Example: "How would you use a popular JavaScript library like React to build a dynamic user interface?"

Design Pattern Prompts:
Example: "Describe the Singleton design pattern and explain its use cases in software development."