Pseudolang Prompts Unleashed
  • 🖥️Introduction to pseudoLang Prompts
    • Purpose of pseudoLang for ChatGPT
    • Benefits of using pseudoLang
    • Limitations and considerations
  • 🏗️Basic Syntax and Structure
    • Defining Functions
  • Specifying Input Types and Output
  • Indicating Actions and Steps
  • Nesting and loops
  • 🎯Examples of pseudoLang Functions
    • Sample Functions
  • 🌟Tips for Writing Effective Pseudolang Prompts
    • Provide Clear Instructions
  • Defining Input and Output Requirements
  • Using Appropriate Syntax and Formatting
  • Experimenting with Different Phrasings
  • Verifying and Refining Generated Outputs
  • 🧠Advanced pseudoLang Techniques
    • Creating Custom Functions
  • Combining Multiple Functions
  • Handling Errors and Exceptions
  • Guiding the Model with Additional Context
  • 🔚Conclusion and Next Steps
    • Further Resources and Learning
    • Community Contributions and Collaboration
  • Ongoing Development and Future Updates
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Combining Multiple Functions

For complex tasks, creating multi-function pseudoLang prompts by chaining or nesting functions within a single prompt can help guide the AI to generate more relevant and accurate responses. Chaining or nesting functions allow you to break down the task into smaller sub-tasks, making it easier for the AI to understand and process each part sequentially or hierarchically.

Chaining functions refers to combining multiple function-like structures within a single prompt, with each function focusing on a specific sub-task. The output of one function can serve as input for the subsequent function, enabling step-by-step processing of the overall task.

Nesting functions, on the other hand, involve placing one function within another. This is useful when a sub-task depends on another function's output, and the entire process needs to be structured hierarchically.

Here's an example of a chained and nested pseudoLang prompt for a complex task:

1. Define function `fetch_article(topic: str) -> str`: 
    a. Search for a relevant article on the given topic 
    b. Return the article text 

2. Define function `summarize_article(article_text: str, length: int) -> str`: 
    a. Summarize the given article to the specified length 
    b. Return the summarized text 

3. Define function `translate_summary(summary: str, target_language: str) -> str`: 
    a. Translate the given summary into the specified target language 
    b. Return the translated text 

4. Process: 
    a. Fetch an article on "climate change" using `fetch_article` 
    b. Summarize the article to 200 words using `summarize_article` 
    c. Translate the summary into French using `translate_summary` 
    d. Return the translated summary 

In this example, three functions are chained together to fetch, summarize, and translate an article. The process is broken down into smaller, manageable sub-tasks, making it easier for ChatGPT to understand the requirements and generate the desired output.

By creating multi-function pseudoLang prompts that chain or nest functions within a single prompt, you can effectively guide ChatGPT in tackling complex tasks and improve the quality and relevance of its responses.

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Last updated 2 years ago