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5. Hands-on Exercises for Basic Prompts

JerryAbout 3 minAI EducationPrompt EngineeringAI PromptsExercisesPractical Learning

In this lesson, we’ll work through hands-on exercises to reinforce the knowledge gained about basic prompts. The exercises will cover multiple AI models, such as text generation, image creation, and coding assistance. By practicing with different types of prompts, you will better understand how to write effective prompts for various AI-driven applications.

Goal:

Create a prompt that asks a language model to generate coherent and meaningful text.

Task:

Write a prompt that instructs the model to generate a short article on the topic of sustainable living.

Example Prompt:

"Write a short article about the importance of sustainable living, including tips on how individuals can reduce their carbon footprint."

Expected Output:

The AI should generate a brief article discussing sustainable living, with actionable tips such as using renewable energy, reducing waste, and recycling.

Code Example (Using OpenAI's GPT-3):

import openai

openai.api_key = 'your-api-key'

prompt = "Write a short article about the importance of sustainable living, including tips on how individuals can reduce their carbon footprint."

response = openai.Completion.create(
  engine="text-davinci-003",
  prompt=prompt,
  max_tokens=150
)

print(response.choices[0].text.strip())

Flow Diagram:

flowchart TD
    A[User Prompt] --> B[Text Generation Model]
    B --> C[Generate Article on Sustainable Living]
    C --> D[Output Generated Text]

Tip:

Be clear in your prompt about the format of the output (e.g., article, essay, list) to ensure the AI generates a structured response.

2. Exercise 2: Writing Prompts for Image Creation

Goal:

Create a detailed text prompt to generate an image using a model like DALL·E.

Task:

Write a prompt that generates an image of a serene beach at sunset with palm trees in the foreground.

Example Prompt:

"Generate an image of a serene beach at sunset, with palm trees in the foreground, soft waves in the ocean, and a clear, vibrant sky."

Flow Diagram:

flowchart TD
    A[User Prompt] --> B[Image Generation Model]
    B --> C[Generate Image of Beach at Sunset]
    C --> D[Output Image]

Key Tip:

Focus on providing visual details like the time of day, colors, and specific objects to make the image generation more accurate.

3. Exercise 3: Prompts for Coding Models

Goal:

Create a prompt that asks a coding assistant to generate a Python function.

Task:

Write a prompt to generate a Python function that checks whether a number is prime.

Example Prompt:

"Write a Python function that takes an integer as input and returns whether it is a prime number."

Expected Output (Generated Code):

def is_prime(n):
    if n <= 1:
        return False
    for i in range(2, int(n**0.5) + 1):
        if n % i == 0:
            return False
    return True

Code Example:

You can use OpenAI's Codex model or GitHub Copilot to generate this code. Here’s how you would integrate it using Codex:

import openai

openai.api_key = 'your-api-key'

prompt = "Write a Python function that takes an integer as input and returns whether it is a prime number."

response = openai.Completion.create(
  engine="code-davinci-002",
  prompt=prompt,
  max_tokens=64
)

print(response.choices[0].text.strip())

Flow Diagram:

flowchart TD
    A[User Prompt] --> B[Coding Assistance Model]
    B --> C[Generate Python Code for Prime Check]
    C --> D[Output Python Code]

Key Tip:

For coding prompts, always specify the programming language and any requirements (e.g., input/output structure) to ensure the model generates accurate code.

4. Exercise 4: Q&A Prompts for Question-Answering Models

Goal:

Create a prompt for a question-answering model like GPT-3 to generate concise and accurate answers.

Task:

Write a prompt that asks a question and instructs the AI to give a brief explanation.

Example Prompt:

"What are the benefits of regular exercise? Provide a brief explanation."

Expected Output:

The AI should provide a concise explanation covering points like improved physical health, mental well-being, and longevity.

Flow Diagram:

flowchart TD
    A[User Question] --> B[Q&A Model]
    B --> C[Retrieve Answer from Model's Knowledge Base]
    C --> D[Output Explanation on Benefits of Exercise]

Key Tip:

For Q&A prompts, keep the question clear and specific, and use phrases like "briefly explain" or "give a detailed explanation" to control the length of the output.

5. Exercise 5: Crafting Prompts for Speech Recognition Models

Goal:

Create a prompt to instruct a speech recognition model to transcribe audio.

Task:

Write a prompt that asks the AI to transcribe an audio file of a business meeting.

Example Prompt:

"Transcribe this audio file of a business meeting, highlighting the key points discussed."

Flow Diagram:

flowchart TD
    A[Audio File] --> B[Speech Recognition Model]
    B --> C[Generate Transcription of Meeting]
    C --> D[Output Text]

Key Tip:

Provide context in the prompt, like asking for key points or summary, to tailor the transcription to your needs.

6. Recap and Best Practices for Basic Prompts

Best Practices for Writing Basic Prompts:

  1. Be Specific: The more details you provide, the better the AI will perform.
  2. Keep it Simple: Avoid overloading the prompt with multiple tasks or complex instructions.
  3. Context Matters: Provide necessary context for accurate responses.
  4. Review Outputs: Iteratively improve your prompts based on the AI’s responses.

7. Conclusion

By working through these exercises, you’ve practiced crafting effective prompts for different AI models. Mastering the art of writing prompts is crucial for getting the best results from AI, whether you're working with text generation, image creation, coding, or speech recognition.

8. References

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