Prompt Engineering Guidebook

A compact guide to writing prompts for generative AI models.

Prompt Engineering Guidebook
Version 2.0 (March 2025) | Download PDF
Prompt Engineering Guidebook Preview

This guidebook provides practical strategies for effective communication with AI systems.

Overview

This guide addresses the challenges facing researchers and practitioners working with generative AI.

Key insight: Using generative AI involves Natural Language Processing (NLP), not human language.
Guide Structure
1. Foundations
Definitions: Understanding LLMs as mathematical models
Limitations: Critical analysis of training data dependencies
Historical Context: Evolution from ELIZA through modern transformers
2. Core Principles
Clarity: Using straightforward, unambiguous language
Context: Providing situational information
Precision: Delimiting inputs with quotation marks, XML tags
Examples: Implementing few-shot prompting
3. Advanced Techniques
✳︎Role Prompting

Strategic persona assignment to align model outputs with specific expertise.

✳︎Multi-shot Prompting
Zero-shot: Task performance without prior examples
One-shot: Single example-guided performance
Few-shot: Multiple example-driven learning
✳︎Reasoning Frameworks
Chain-of-Thought (CoT): Linear, step-by-step problem decomposition
Tree-of-Thought (ToT): Hierarchical exploration of multiple reasoning paths
Graph-of-Thought (GoT): Network-based reasoning allowing cycles
4. Practical Applications

The guide includes detailed examples for:

  • Academic writing and peer review organization
  • Conference invitation personalization
  • Literary analysis with structured outputs
Key Contributions
Methodological Framework

This guide introduces a systematic approach based on four iterative steps:

  1. Define the Goal: Establishing specific, measurable criteria
  2. Craft the Prompt: Implementing clarity, context, precision
  3. Generate & Evaluate: Testing outputs against criteria
  4. Refine: Iterating based on performance assessment
Critical Perspective on AI Limitations

This resource emphasizes critical understanding:

  • Training data bias propagation
  • Hallucination patterns
  • Distinction between probability-based responses and factual accuracy
Target Audience
Researchers and Academics

Seeking to integrate AI tools into scholarly workflows

Digital Humanities Practitioners

Working at technology-humanities intersection

Graduate Students

Developing computational literacy alongside disciplinary expertise

Pedagogical Approach

This guidebook treats AI interaction as computational thinking that requires:

  • Understanding system capabilities and constraints
  • Systematic approach to problem decomposition
  • Iterative refinement based on empirical results
Technical Specifications
Format:
PDF guidebook with visual examples and practical examples
Version:
2.0 (March 2025)