📚 "A Survey of Prompt Engineering Methods in Large Language Models" - LLM

A Survey of Prompt Engineering Methods in Large Language Models

Curiosity: What are the most effective prompt engineering techniques? How can we systematically understand and apply prompt engineering methods across different NLP tasks?

This comprehensive survey is the ultimate guide to prompt engineering, providing a structured taxonomy, comprehensive vocabulary, and analysis of 58 text-only and 40 multimodal prompting techniques.

Paper: https://arxiv.org/abs/2407.12994

Survey Overview

graph TB
    A[Prompt Engineering Survey] --> B[44 Research Papers]
    A --> C[39 Prompting Methods]
    A --> D[29 NLP Tasks]
    
    B --> E[Analysis]
    C --> F[Taxonomy]
    D --> G[Task Classification]
    
    E --> H[Structured Understanding]
    F --> H
    G --> H
    
    style A fill:#e1f5ff
    style H fill:#d4edda

Key Features

Feature	Description	Impact
Standardization	Structured taxonomy of techniques	⬆️ Clarity
Comprehensive	39 prompting methods analyzed	⬆️ Coverage
Task-Specific	29 NLP tasks covered	⬆️ Practicality
SoTA Methods	Best methods per task	⬆️ Performance

Survey Scope

Retrieve: Extensive analysis of prompt engineering research.

Analysis Coverage:

• 44 Research Papers: Comprehensive literature review
• 39 Prompting Methods: Diverse techniques analyzed
• 29 NLP Tasks: Wide range of applications
• 58 Text-Only Techniques: Text-focused methods
• 40 Multimodal Techniques: Cross-modal approaches

What Makes This Survey Special

Innovate: This survey provides structured understanding and standardization.

Unique Contributions:

1. Standardization: Structured taxonomy of prompt engineering techniques
2. Task Classification: NLP tasks organized by dataset and strategy
3. SoTA Methods: Best prompting methods identified for each task
4. Comprehensive Vocabulary: 33 terms defined and explained

Prompt Engineering Techniques

Text-Only Techniques (58 methods):

• Zero-shot prompting
• Few-shot prompting
• Chain-of-thought
• Self-consistency
• And 54 more…

Multimodal Techniques (40 methods):

• Vision-language prompts
• Audio-text prompts
• Cross-modal reasoning
• And 37 more…

Taxonomy Structure

graph TB
    A[Prompt Engineering] --> B[Text-Only]
    A --> C[Multimodal]
    
    B --> B1[Zero-Shot]
    B --> B2[Few-Shot]
    B --> B3[Chain-of-Thought]
    B --> B4[Advanced]
    
    C --> C1[Vision-Language]
    C --> C2[Audio-Text]
    C --> C3[Cross-Modal]
    
    style A fill:#e1f5ff
    style B fill:#fff3cd
    style C fill:#d4edda

NLP Task Coverage

Retrieve: 29 different NLP tasks analyzed with optimal prompting strategies.

Task Categories:

• Classification tasks
• Generation tasks
• Question answering
• Summarization
• Translation
• And 24 more…

For Each Task:

• Dataset information
• Applied prompting strategies
• SoTA methods identified
• LLM models used

Who Should Read This

Audience	Benefit	Use Case
AI Researchers	Latest trends in prompting	Research and development
NLP Developers	Task-optimized methods	Application development
LLM Engineers	Systematic approaches	Production systems
AI Educators	Comprehensive materials	Teaching and training

Key Takeaways

Retrieve: This survey provides a comprehensive, structured overview of prompt engineering methods, analyzing 44 papers, 39 methods, and 29 NLP tasks.

Innovate: By understanding the taxonomy and SoTA methods, you can select optimal prompting strategies for your specific tasks, improving LLM performance systematically.

Curiosity → Retrieve → Innovation: Start with curiosity about prompt engineering, retrieve insights from this comprehensive survey, and innovate by applying optimal methods to your LLM applications.

Why It Matters: Prompt engineering is essential for effectively using Claude, ChatGPT, and other LLMs. This survey helps you discover techniques you might have missed, with clear categorization for easy reference.

Next Steps:

• Read the full paper
• Explore techniques for your tasks
• Experiment with SoTA methods
• Apply to your LLM applications