Stop Writing Ineffective Prompts! The 18 Most Practical Prompt Engineering Techniques of 2024 (Part 2: Final)

The 18 Most Practical Prompt Engineering Techniques of 2024 (Part 2: Final)

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Preface

In the previous article, we introduced 9 foundational and widely used prompting techniques.
Today, we’ll dive into 9 more advanced and cutting-edge techniques, representing the forefront of prompt engineering in 2024.
These techniques not only enhance model reasoning but also support automated prompt optimization and multimodal interactions.
Click here for Part 1 »

Core Prompting Techniques (Advanced)

10. Automatic Reasoning and Tool-use

This is one of the most important prompting techniques of 2024. It allows the model to autonomously decide whether to use external tools while combining reasoning and tools freely.
Use Case: Complex Data Analysis
Traditional format:
Automated Reasoning + Tool Use format:

11. Automatic Prompt Engineer (APE)

APE is a meta-prompting technique that automatically generates and optimizes high-efficiency prompts. It saw wide adoption in 2024, especially in enterprise-level AI applications.
Use Case: Optimizing Customer Service Templates
Initial prompt:
Optimized by APE:

12. Active-Prompt

This technique dynamically adjusts prompts based on previous responses to improve output quality.
Use Case: Writing Assistance
Round 1 prompt:
Adjusted prompt based on feedback:

13. Directional Stimulus Prompting

This technique uses structured prompts to guide the model's thinking and answering toward specific goals. It has improved output control significantly in 2024.
Use Case: Strategy Analysis
Simple prompt:
Directional prompt:

14. Program-Aided Language Models (PAL)

PAL integrates programming with natural language processing, enabling models to solve complex problems via structured reasoning.
Use Case: Complex Scheduling
Traditional format:

15. Reflexion

Reflexion lets the model reflect and revise its own output, improving quality through iterative feedback. A major technique that gained traction in 2024.
Use Case: Writing Improvement

16. Multimodal CoT

This technique combines visual and textual reasoning, allowing the model to process images + text jointly. It significantly improves understanding and generation in 2024.
Use Case: Architecture Analysis

17. Graph Prompting

This technique guides model reasoning using structured graphs to map knowledge. It shows great potential in 2024.
Use Case: Product R&D Planning

Summary of This Article

In this final article, we introduced 9 advanced prompting techniques representing the latest trends in prompt engineering for 2024. They fall into these categories:
  1. Prompt Optimization:
      2. APE, Active-Prompt – improve efficiency
  1. Tool Integration:
      2. Auto Reasoning + Tool-use, PAL – expand capabilities
  1. Multimodal Reasoning:
      2. Multimodal CoT, Graph Prompting – enhance reasoning depth
  1. Self-Revision:
      2. Reflexion, ReAct – improve output quality
  1. Structured Guidance:
      2. Directional Stimulus – improve controllability
These techniques help developers better control and utilize large language models. In practice, they are often used in combination for best results.

Prompt Engineering Technique System Summary

In these two articles, we introduced all 18 prompting techniques. Now, let’s organize them into a system for better understanding.

I. Technique Categories

1. Basic Prompting

Zero-shot, Few-shot – best for simple, direct tasks

2. Reasoning Techniques

Includes CoT, Tree of Thoughts, Graph Prompting, Tool-use, PAL, ReAct – improve reasoning and problem-solving

3. Knowledge Techniques

Generate Knowledge, RAG, Multimodal CoT – enhance knowledge accuracy and depth

4. Optimization Techniques

APE, Active-Prompt, Meta Prompting, Reflexion, Self-Consistency – improve quality and reliability

5. Task Structuring

Directional Stimulus, Prompt Chaining – structure and organize complex tasks

II. Practical Application Suggestions

1. Task Matching

  • Simple tasks → Zero-shot or Few-shot
  • Complex reasoning → CoT or Tree of Thoughts
  • Knowledge-heavy tasks → RAG, Generate Knowledge
  • Output quality → Reflexion, Self-Consistency
  • Workflow control → Prompt Chaining

2. Strategy Combinations

  • Base + Reasoning → Zero/Few + CoT
  • Knowledge → RAG + Generate Knowledge
  • Quality → Self-Consistency + Reflexion
  • Complex tasks → Tool-use + Prompt Chaining

3. Trend Forecasting

  • Automation: APE will continue to rise
  • Multimodal: Integration of image, text, and graphs will be key
  • Output quality: More emphasis on feedback, revision, trust
  • Tools: Tight tool integration will deepen

Conclusion

Prompt engineering is rapidly evolving. These 18 techniques represent today’s best practices.
In real-world use, the key is flexible selection and combination based on task needs.
As large model technology progresses, we expect to see even more innovation in this field.
We hope this structured guide helps you understand, apply, and master prompt engineering to fully unlock the potential of AI models.
 
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