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    Advanced ApplicationsPrompt Engineering

    Effectiveness Evaluation

    This tutorial focuses on methods and techniques for evaluating the effectiveness of prompts in AI language models. We'll explore various metrics for measuring prompt performance and discuss both manual and automated evaluation techniques.

    As prompt engineering becomes increasingly crucial in AI applications, it's essential to have robust methods for assessing prompt effectiveness. This enables developers and researchers to optimize their prompts, leading to better AI model performance and more reliable outputs.

    What you'll learn

    • 1
      Metrics for measuring prompt performance
    • 2
      Manual evaluation techniques
    • 3
      Automated evaluation techniques
    • 4
      Practical examples using OpenAI and LangChain

    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.

    Get it on Amazon

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

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