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Unlocking the Secrets of Prompt Engineering

Unlocking the Secrets of Prompt Engineering

By : Gilbert Mizrahi
4.6 (16)
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Unlocking the Secrets of Prompt Engineering

Unlocking the Secrets of Prompt Engineering

4.6 (16)
By: Gilbert Mizrahi

Overview of this book

Unlocking the Secrets of Prompt Engineering is your key to mastering the art of AI-driven writing. This book propels you into the world of large language models (LLMs), empowering you to create and apply prompts effectively for diverse applications, from revolutionizing content creation and chatbots to coding assistance. Starting with the fundamentals of prompt engineering, this guide provides a solid foundation in LLM prompts, their components, and applications. Through practical examples and use cases, you'll discover how LLMs can be used for generating product descriptions, personalized emails, social media posts, and even creative writing projects like fiction and poetry. The book covers advanced use cases such as creating and promoting podcasts, integrating LLMs with other tools, and using AI for chatbot development. But that’s not all. You'll also delve into the ethical considerations, best practices, and limitations of using LLM prompts as you experiment and optimize your approach for best results. By the end of this book, you'll have unlocked the full potential of AI in writing and content creation to generate ideas, overcome writer's block, boost productivity, and improve communication skills.
Table of Contents (18 chapters)
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1
Part 1:Introduction to Prompt Engineering
4
Part 2:Basic Prompt Engineering Techniques
8
Part 3: Advanced Use Cases for Different Industries
13
Part 4:Ethics, Limitations, and Future Developments

Types of LLM prompts

LLM prompts can be categorized based on several aspects, such as their purpose, format, and level of detail provided.

One way of classifying them is by the type of information that’s being prompted:

  • Zero-shot prompting: This is a technique that allows an LLM to generate responses to tasks that it has not been specifically trained for. In this technique, the LLM is provided with an input text and a prompt that describes the expected output from the model in natural language. The LLM then uses its knowledge to generate a response that is consistent with the prompt.

    For example, if you provide the LLM with the input text Write a poem about love, and the prompt The poem should be beautiful and romantic, the LLM might generate the following response:

    Love is a many splendored thing,
    It's the April rose that only grows in the early spring.
    Love is nature's way of giving,
    A reason to be living.
    Love is a many splendored thing,
    It's the golden...

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