Skip to main content

Prompting Techniques

Updated Jul 12, 2023 ·

Overview​

Different prompting techniques help guide how a model understands and responds to tasks. Some methods rely on examples, while others guide reasoning or set roles for better output.

  • Zero-shot learning uses no examples and relies on prior knowledge
  • One-shot learning uses a single example to guide the response
  • Few-shot learning uses multiple examples to show patterns

Zero-shot Learning​

Zero-shot learning means giving the model a task without any examples. It relies fully on what the model already knows.

Example:

Write a poem about the calmness of mountains.

The model uses its Prompting to generate a response without any prior pattern shown.

One-shot Learning​

One-shot learning provides a single example before asking the actual question. This helps the model follow a pattern more easily.

Example:

Paris is the capital of France.  
What is the capital of Japan?

The model uses the example structure to guide its answer.

Few-shot Learning​

Few-shot learning gives multiple examples so the model can learn a pattern more clearly. This improves consistency in formatting and style.

Example:

France – 🇫🇷 Paris  
Germany – 🇩🇪 Berlin  
Japan – 🇯🇵 Tokyo  
What is the capital of Malaysia?

The model follows the same format for the new input.

Pattern Matching and Recognition​

Few-shot learning helps the model recognize patterns and repeat them in new outputs.

  • Learns formatting styles from examples
  • Copies structure for emails or reports
  • Generates consistent outputs based on patterns

This makes the model more reliable when working with structured tasks like templates or formatted content.

Chain of Thought (CoT) Prompting​

Chain of Thought prompting helps the model solve problems step by step instead of answering in one step. This improves reasoning for complex tasks.

  • Zero-shot CoT uses a simple instruction like “think step by step”
  • One-shot CoT provides an example of step-by-step reasoning
  • Structured CoT breaks the task into guided steps

In the example below, the model is encouraged to break down the problem before giving the final answer:

Write a Python function to check if a number is a palindrome. Think step by step.

Reasoning models don't need explicit COT prompts

Modern reasoning models are designed to handle step-by-step logic internally.

They often do not need explicit Chain of Thought prompts.

  • Break problems into internal steps automatically
  • Self-check intermediate reasoning
  • Produce more accurate outputs for coding and logic tasks

This reduces the need for manual step-by-step prompting in many cases.

System Roles​

A system role defines the behavior or identity the model should follow before answering a prompt. It helps shape tone, depth, and style.

  • Defines the model’s role in the conversation
  • Improves consistency in responses
  • Adapts output for different use cases

Example:

You are a friendly programming tutor.  
Explain concepts in simple terms with examples and analogies.  
Highlight common mistakes to avoid.

In the example above, the system roles:

  • System prompt:

    You are a friendly programming tutor.

  • User prompt:

    Explain concepts in simple terms with examples and analogies. Highlight common mistakes to avoid.

Another example for debugging:

You are a senior software engineer.  
First explain what the code is trying to do.  
Then identify possible issues and suggest fixes.