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  1. Skills Directory
  2. Prompt Engineering

Prompt Engineering

Basic

Prompt Engineering (Basic)

These concepts cover the fundamental structure and basic techniques of prompt engineering. They are necessary for writing effective prompts and understanding how to interact with a language model effectively.

  1. Basic Prompt Structure

    • Activities involved: Writing simple prompts with and without additional parameters.

    • Reason: Understanding the basic structure is essential for creating effective prompts.

    • Example Task: Write a prompt that asks the model to generate a short story without any additional parameters.

  2. Writing Clear and Direct Prompts

    • Activities involved: Crafting straightforward and explicit instructions.

    • Reason: Clear instructions lead to more accurate and relevant responses from the model.

    • Example Task: Write a prompt that instructs the model to summarize a paragraph in one sentence.

  3. System Prompt

    • Activities involved: Using system prompts to set instructions for a conversation.

    • Reason: System prompts guide the model to follow specific instructions consistently.

    • Example Task: Write a system prompt that instructs the model to always respond politely.

  4. Role Prompting

    • Activities involved: Prompting the model to assume a specific role with all necessary context.

    • Reason: Providing detailed context enhances the model's ability to perform tasks within the specified role.

    • Example Task: Write a prompt that asks the model to act as a historical figure and answer questions from that perspective.

  5. Few-shot Prompting

    • Activities involved: Providing examples within the prompt to guide the model's behavior.

    • Reason: Examples help the model understand the expected output format and behavior.

    • Example Task: Write a few-shot prompt that includes examples of correct and incorrect ways to format a bibliography entry.

  6. Zero-shot, One-shot, and N-shot Prompting

    • Activities involved: Using varying numbers of examples to guide the model's responses.

    • Reason: Different numbers of examples can influence the accuracy and format of the model's output.

    • Example Task: Write zero-shot, one-shot, and few-shot prompts for generating email responses based on different levels of example inputs.