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Prompt Engineering Book

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Kamran Dar
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0% found this document useful (0 votes)
227 views4 pages

Prompt Engineering Book

Uploaded by

Kamran Dar
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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ompt Engineering for Software Developers: A Practical Guide to Scaling with LL

By: Your Name

This book serves as a comprehensive guide to mastering prompt engineering, helping software

developers leverage the power of large language models (LLMs) to scale their productivity and

projects.
Introduction

Importance of Prompt Engineering

Prompt engineering is transforming the way software developers build applications by simplifying

interactions with complex language models.

Role of LLMs in Modern Software Development

LLMs like GPT and Gemini-1.5 can automate tasks such as code generation, debugging, and

creating technical documentation.

Overview of the Book Structure

This book will take you through the fundamentals of prompt engineering, advanced techniques, and

real-world applications.
Chapter 1: Fundamentals of Prompt Engineering

What is Prompt Engineering?

Prompt engineering involves crafting queries or instructions that maximize the effectiveness of

LLMs.

How Large Language Models Work

LLMs process input as tokens and generate outputs based on probabilistic predictions, influenced

by training data and model architecture.

Key Terminologies: Tokens, Context, Parameters

- Tokens: Units of text (e.g., words or characters).

- Context: The surrounding information the model uses for inference.

- Parameters: Internal weights determining the model's capabilities.

Use Cases of LLMs in Software Development

LLMs can be used for automating code reviews, generating SQL queries, and creating chatbots.
Chapter 2: Crafting Effective Prompts

Characteristics of a Good Prompt: Clarity, Specificity, Context

An effective prompt ensures that the model understands the task, reducing ambiguity.

Prompt Types: Declarative, Interrogative, Directive

- Declarative: Provide detailed instructions.

- Interrogative: Ask questions.

- Directive: Specify actions to perform.

Advanced Techniques: Chain of Thought, Few-Shot, Zero-Shot Learning

These techniques improve the model's reasoning and contextual understanding.

Practical Example:

"Generate a Python function to reverse a string." vs. "Write a Python function that takes a string

input and returns the reversed string."

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