Skip to main content

4. Examples of Basic Prompts in Different AI Models

JerryAbout 4 minAI EducationPrompt EngineeringAI PromptsModel ExamplesGPT-3DALL-E

In this lesson, we will explore how basic prompts are used in different AI models, including text generation, image creation, coding assistance, and more. Understanding the variety of prompt types will provide a foundation for crafting effective prompts across multiple AI-driven applications.

1. Overview of AI Models

Before diving into specific examples, let’s briefly outline some common types of AI models where prompts play a critical role:

  • Text Generation Models: GPT, ChatGPT, and other language models.
  • Image Generation Models: DALL·E, Stable Diffusion.
  • Coding Assistance Models: GitHub Copilot, OpenAI Codex.
  • Question-Answering Models: BERT, GPT-3.
  • Other Specialized AI Models: Translation (DeepL, Google Translate), Speech Recognition (Whisper).

2. Basic Prompts in Text Generation Models

Example 1: Simple Text Generation with GPT-3

A text generation AI model like GPT-3 or ChatGPT responds to natural language prompts. Here, we’ll look at basic prompts that instruct the model to generate text.

"Write a short story about a cat who discovers a hidden forest in a city."
  • Prompt Breakdown:
    • Instruction: Write a story.
    • Subject: A cat.
    • Scenario: Discovers a hidden forest in the city.

This prompt is specific enough to guide the model while allowing room for creative freedom.

Example 2: Requesting a Definition

"What is quantum computing?"
  • Prompt Breakdown:
    • Instruction: Provide a definition.
    • Subject: Quantum computing.

Flow Diagram for Text Generation:

flowchart TD
    A[User Prompt] --> B[AI Model]
    B --> C[Generate Coherent Text]
    C --> D[Output Text Response]

Common Mistakes:

  • Using vague terms like "Explain technology" instead of narrowing the focus.
  • Not specifying format (story, definition, etc.).

Key Tip:

Use simple, direct language, and be clear about the type of response you expect from the model.

3. Basic Prompts in Image Generation Models

Example 1: Creating an Image with DALL·E

DALL·E is an AI model that generates images from text-based prompts. The prompt needs to clearly describe the desired image.

"Create an image of a futuristic city skyline with flying cars."
  • Prompt Breakdown:
    • Instruction: Create an image.
    • Scene: Futuristic city skyline.
    • Objects: Flying cars.

Example 2: Simple Object Creation

"Generate an image of a red apple on a wooden table."
  • Prompt Breakdown:
    • Object: Red apple.
    • Environment: Wooden table.

Flow Diagram for Image Generation**:

flowchart TD
    A[User Prompt] --> B[AI Image Model]
    B --> C[Generate Image Based on Prompt]
    C --> D[Output Image]

Common Mistakes:

  • Including too many details in a single prompt, making it difficult for the model to capture everything accurately.
  • Not specifying important characteristics such as color, environment, or object size.

Key Tip:

When crafting prompts for image models, focus on essential details and avoid overloading the prompt with unnecessary information.

4. Basic Prompts in Coding Assistance Models

Example 1: Requesting Code from Codex

AI models like OpenAI Codex and GitHub Copilot can generate code based on natural language prompts. The more specific your prompt, the better the output.

"Write a Python function that calculates the factorial of a number."
  • Prompt Breakdown:
    • Instruction: Write a Python function.
    • Task: Calculate factorial.

Generated Code:

def factorial(n):
    if n == 0 or n == 1:
        return 1
    else:
        return n * factorial(n-1)

Example 2: Simple Code Refactoring

"Refactor this code to be more efficient: 
def sum_of_squares(numbers):
    return sum([n**2 for n in numbers])"
  • Prompt Breakdown:
    • Instruction: Refactor code.
    • Task: Optimize for efficiency.

Flow Diagram for Code Generation**:

flowchart TD
    A[User Prompt] --> B[AI Coding Model]
    B --> C[Generate Code Snippet]
    C --> D[Output Code]

Common Mistakes:

  • Using vague terms like "Fix this code" without specifying what needs to be improved (speed, readability, etc.).
  • Asking for complex multi-task operations in a single prompt.

Key Tip:

When asking for code, be as specific as possible about the task and language, and provide example code where necessary.

5. Basic Prompts in Question-Answering Models

Example 1: Simple Q&A with BERT or GPT-3

Question-answering models take in questions and generate concise answers. Basic prompts often include the question followed by clarifying context.

"What is the capital of France?"
  • Prompt Breakdown:
    • Instruction: Ask a factual question.
    • Subject: Capital of France.

AI Output:

"Paris."

Example 2: Requesting Explanations

"Explain how photosynthesis works."
  • Prompt Breakdown:
    • Instruction: Explain a scientific process.
    • Subject: Photosynthesis.

Flow Diagram for Q&A Models**:

flowchart TD
    A[User Question] --> B[AI Q&A Model]
    B --> C[Retrieve or Generate Answer]
    C --> D[Output Answer]

Common Mistakes:

  • Asking broad or vague questions like "Explain life" without providing enough context for a focused response.
  • Including too many nested questions in a single prompt.

Key Tip:

Always ensure the question is clear and unambiguous, and keep the focus on one key point at a time.

6. Basic Prompts in Speech Recognition Models

Example 1: Transcribing Speech with Whisper

Speech recognition models like Whisper transcribe audio into text. You can prompt these models by submitting audio clips and expecting transcriptions.

"Transcribe this audio file of a podcast episode."
  • Prompt Breakdown:
    • Instruction: Transcribe audio.
    • Context: Podcast episode.

Example 2: Identifying Speaker in Transcription

"Transcribe and identify speakers in this interview recording."
  • Prompt Breakdown:
    • Instruction: Transcribe audio.
    • Task: Identify speakers.

Flow Diagram for Speech Recognition Models:

flowchart TD
    A[Audio File] --> B[Speech Recognition Model]
    B --> C[Generate Transcription]
    C --> D[Output Text with Speaker Identification]

Common Mistakes:

  • Providing low-quality audio files, which can reduce transcription accuracy.
  • Asking for too many tasks (e.g., transcription and translation) in a single prompt.

Key Tip:

Provide clear instructions for the specific transcription task, and ensure the input data (audio file) is of high quality for best results.

7. Exercise: Crafting Basic Prompts for Different AI Models

Exercise 1: Writing Prompts for Text Generation

  • Task: Write a prompt to ask a language model for an article on the benefits of meditation.

Exercise 2: Writing Prompts for Image Creation

  • Task: Write a prompt to generate an image of a futuristic space station with robots.

Exercise 3: Writing Prompts for Coding Models

  • Task: Write a prompt to request Python code that calculates the area of a circle.

8. Conclusion

Crafting basic prompts is an essential skill when working with AI models across different domains. Whether you’re working with text generation, image creation, or coding assistants, being precise in your language and knowing the capabilities of the model will improve your outputs.

9. References

Last update:
Copyright © 2024 Jerry