Stop Writing Ineffective Prompts!18 Most Practical Prompt Engineering Techniques of 2024 (Part 1)

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Core Prompting Techniques Explained

1. Zero-shot Prompting

Zero-shot Prompting is the most basic prompting technique. It does not require providing example inputs to guide the model. This technique relies on the model’s inherent abilities and uses task instructions alone to guide output.

Application Example: Text Classification

Simple instruction format:

Zero-shot prompt format:

Technique Summary:

The advantage of Zero-shot Prompting lies in its simplicity and not requiring example data. (However, compared to Few-shot Prompting, its accuracy may be lower.)

This method is best for straightforward tasks with clear objectives or when examples are hard to provide.

2. Few-shot Prompting

Few-shot Prompting improves model understanding by providing a few examples of the target task. It helps the model better grasp task formats and requirements. It’s especially useful in tasks that follow a structured format.

Application Example: Custom-formatted Text Generation

Simple instruction format:

Few-shot prompt format:

Technique Summary:

Few-shot is better than Zero-shot at generating predictable, structured outputs and is ideal for tasks that need formatting or specific output style.

However, it requires thoughtful example design and increases prompt length.

3. Chain-of-Thought Prompting

Chain-of-Thought (CoT) Prompting is a technique that guides the model to show its reasoning steps. By making the model think step by step, it improves accuracy for complex problem solving.

Application Example: Solving Math Word Problems

Simple instruction format:

CoT prompt format:

Technique Summary:

Chain-of-Thought is great for multi-step reasoning problems. It improves reasoning transparency and is easier to verify.

It works well with Few-shot and allows better output control for logic-heavy tasks.

4. Meta Prompting

Meta Prompting is an advanced technique where the model first generates and refines the prompt itself, then uses the refined prompt to complete the task, aiming for the best result.

Application Example: Essay Writing

Simple instruction format:

Meta prompt format:

Example of refined prompt:

Technique Summary:

Meta Prompting improves final output quality by:

Refining prompts to enhance precision

Clarifying task expectations

Structuring complex content

Allowing iterative optimization

Best for:

Complex writing tasks

Structured content generation

Analytical tasks with multiple components

High-quality output needs

5. Self-Consistency Prompting

Self-Consistency improves output reliability by generating multiple responses and comparing results for consistency. It’s great for tasks requiring high accuracy.

Application Example: Solving Math Problems

Simple instruction format:

Self-Consistency prompt format:

Technique Summary:

Self-Consistency ensures output reliability through cross-verification. Ideal for math, logic, and precise reasoning tasks.

6. Generate Knowledge Prompting

Generate Knowledge Prompting lets the model first generate relevant knowledge, and then use it to answer questions or perform tasks. It improves output depth and factual accuracy.

Application Example: Writing Technical Content

Simple instruction format:

Generate Knowledge prompt format:

Technique Summary:

Generate Knowledge Prompting enhances content depth and accuracy. Compared to direct writing, it ensures completeness and professionalism.

Best for domains requiring precise and well-organized knowledge.

7. Prompt Chaining

Prompt Chaining breaks complex tasks into sub-tasks and guides the model through a series of linked prompts.

Application Example: Translating and Summarizing an Article

Simple instruction format:

Prompt Chaining format:

Technique Summary:

Prompt Chaining is ideal for multi-step tasks. It provides more control and better structure than single prompts.

Can be combined with other techniques like CoT, but requires managing each step’s quality carefully.

8. Tree of Thoughts

Tree of Thoughts (ToT) is an advanced version of Chain-of-Thought. It builds a thought tree to explore multiple solutions and choose the best.

Application Example: Solving Complex Design Problems

Simple instruction format:

Tree of Thoughts format:

Technique Summary:

Tree of Thoughts gives a structured reasoning path for creative or open-ended tasks.

It helps explore alternatives but requires more planning and time.

9. Retrieval Augmented Generation (RAG)

RAG combines external knowledge retrieval with generation to improve factual accuracy and relevance.

Application Example: Answering Expert-Level Questions

Simple instruction format:

RAG prompt format:

Technique Summary:

RAG improves output accuracy by incorporating external data. Compared to pure generation, it ensures trustworthy responses but relies on retrieval quality.

Summary

This article detailed 9 essential prompt engineering techniques. They can be grouped into 5 categories:

Basic Prompting (Zero-shot, Few-shot): For simple, direct tasks

Reasoning Prompting (Chain-of-Thought, Tree of Thoughts): For multi-step complex problems

Knowledge Generation (Generate Knowledge, RAG): For factual and rich content generation

Task Optimization (Meta Prompting, Prompt Chaining): For improving task efficiency and structure

Reliability Assurance (Self-Consistency): For consistent and trustworthy results