Prompt Engineering Best Practices for Instruction-Tuned LLM

"Prompt Engineering Best Practices for Instruction-Tuned LLM" is a comprehensive guide designed to equip readers with the essential knowledge and tools to master the fine-tuning and prompt engineering of large language models (LLMs). The book covers everything from foundational concepts to advanced applications, making it an invaluable resource for anyone interested in leveraging the full potential of instruction-tuned models.

The first part, Introduction to LLM Instruction Fine Tuning, offers a deep dive into the core concepts behind instruction fine-tuning. As LLMs become increasingly critical in various industries, understanding how to tailor them to specific tasks is vital. This section introduces readers to the nuances of single-task versus multi-task fine-tuning, scaling these models, and evaluating their performance, culminating in a hands-on guide for applying instruction fine-tuning to summarization tasks.

The second part, Prompt Engineering Guide & Best Practices, shifts focus to the art of crafting effective prompts. Prompt engineering allows users to control how LLMs respond, making it essential to ensure precision and reliability. This part covers everything from basic prompt design to more complex techniques like Chain of chain-of-thought reasoning, transforming text, and iterating on prompts for enhanced results. Each chapter is filled with actionable strategies for optimizing prompts across diverse use cases, ensuring LLMs are used to their full potential.

In the final part, Building Projects with Prompt Engineering, the book moves from theory to practice, guiding readers through the development of real-world LLM-powered applications. From creating intelligent chatbots to designing end-to-end customer service systems, this section provides step-by-step instructions to turn prompt engineering concepts into functional, deployable tools.

Table of Contents:

Part I: Introduction to LLM Instruction Fine Tuning

1. Overview of Instruction Fine Tuning
2. Single Vs Multi-Task LLM Instruction Fine-Tuning
3. Overview of Scaling Instruction-Tuned LLMs
4. How Can We Evaluate Instruction Tuned LLM?
5. Instruction Fine-Tuning LLM for Summarization: Step-by-Step Guide

Part II: Prompt Engineering Guide & Best Practices

1. Prompt Engineering Guidelines
2. Iterative Prompt Development
3. Text Summarization & Information Retrieval
4. Textual Inference & Sentiment Analysis
5. Text Transforming & Translation
6. Text Expansion & Generation
7. Chain of Thought Reasoning
8. LLM Output Validation & Evaluation

Part III: Building Projects with Prompt Engineering

1. Building Chatbots Using Prompt Engineering
2. Building an End-to-End Customer Service System
3. Testing Prompt Engineering-Based LLM Applications