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    Fundamental ConceptsPrompt Engineering

    Prompt Templates and Variables

    This tutorial provides a comprehensive introduction to creating and using prompt templates with variables in the context of AI language models. It focuses on leveraging Python and the Jinja2 templating engine to create flexible, reusable prompt structures that can incorporate dynamic content. The tutorial demonstrates how to interact with OpenAI's GPT models using these advanced prompting techniques.

    As AI language models become increasingly sophisticated, the ability to craft effective prompts becomes crucial for obtaining desired outputs. Prompt templates and variables offer several advantages: 1. Reusability: Templates can be reused across different contexts, saving time and ensuring consistency. 2. Flexibility: Variables allow for dynamic content insertion, making prompts adaptable to various scenarios. 3. Complexity Management: Templates can handle complex structures, including conditional logic and loops, enabling more sophisticated interactions with AI models. 4. Scalability: As applications grow, well-structured templates make it easier to manage and maintain large number…

    What you'll learn

    • 1
      PromptTemplate Class: A custom class that wraps Jinja2's Template class, providing a simple interface for creating and using templates.
    • 2
      Jinja2 Templating: Utilization of Jinja2 for advanced templating features, including variable insertion, conditional statements, and loops.
    • 3
      OpenAI API Integration: Direct use of the OpenAI API for sending prompts and receiving responses from GPT models.
    • 4
      Variable Handling: Techniques for incorporating variables into templates and managing dynamic content.
    • 5
      Conditional Logic: Implementation of if-else statements within templates to create context-aware prompts.
    • 6
      Advanced Formatting: Methods for structuring complex prompts, including list formatting and multi-part instructions.

    About this tutorial

    This hands-on Jupyter notebook is part of Prompt Engineering, a free open-source repository by Nir Diamant covering prompt engineering techniques with runnable code examples and detailed explanations.

    Free and open-sourceRunnable Jupyter notebookActive community support
    Go deeper · By the bestselling author of RAG Made Simple

    Prompt Engineering: Zero to Hero

    The expanded book version of this repo: 22 prompt-engineering techniques explained in depth, with hands-on exercises that take you from fundamentals to advanced steering.

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    Kindle $9.99 · Paperback $24.99 · Free with Kindle Unlimited

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