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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

Moving beyond APIs – building custom LLM pipelines with LangChain

LangChain is an open source Python and JavaScript library for building workflows and systems using LLMs such as GPT-3.

Created by Harrison Chase, LangChain initially focused on streamlining integration with OpenAI APIs. It has since expanded to support other LLMs, including models such as Anthropic’s Claude.

The library implements techniques from the ReAct paper published in 2022. ReAct demonstrates prompting methods that allow LLMs to engage in reasoning by maintaining a chain-of-thought context. Models can also take action by leveraging tools such as internet search to gather information.

This combination, referred to as ReAct, enables LLMs to solve problems more effectively by thinking through logic and bringing in outside knowledge. LangChain codifies these techniques into developer-friendly frameworks.

LangChain supports a wide range of LLMs from various providers, including the following...

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