> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langdock.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Prompting Techniques

> Learn the three fundamental prompting techniques: zero-shot, few-shot, and chain-of-thought prompting.

There are three fundamental techniques you can use when prompting LLMs. Understanding when and how to use each will help you craft more effective prompts.

## Zero-shot Prompting

Zero-shot prompting means asking the model to perform a task without providing any examples. Since these models have been trained on massive datasets, their internal knowledge makes them capable of performing a large number of tasks without demonstrations.

**Think of it like:** Asking a guitar player to play the piano, even though they've never played piano before. They'd apply their previous knowledge about music and instruments to figure it out.

**Example:**

*Prompt:*

```
Classify the text into the categories of satisfied, neutral or unsatisfied.

Text: I was happy with the customer support today.
```

*Output:*

```
Satisfied
```

**When to use:** Perfect for general tasks like classification, translation, and answering questions with established knowledge. Most prompts we use are, by default, zero-shot prompts.

***

## Few-shot Prompting

Few-shot prompting means providing demonstrations of how to perform a task. Instead of relying only on the AI model's general training, you give specific examples that guide the model to perform your exact task with higher quality.

**Think of it like:** Showing a guitarist a few piano pieces before asking them to play piano for the first time.

**Example:**

*Prompt:*

```
I was happy with the customer support today - satisfied
The product is horrible! - very unsatisfied
This is one of the best products I have ever used - very satisfied
This is such a great product! -
```

*Output:*

```
Very Satisfied
```

**When to use:**

* Complex or nuanced tasks where format matters
* When you need consistent output structure
* 3-4 examples typically work best

**Important limitation:** For complex reasoning tasks, few-shot prompting hits its limits. In those cases, combine it with chain-of-thought principles for better results.

***

## Chain-of-Thought Prompting

Chain-of-thought prompting improves reasoning quality by forcing the model to externalize its thinking process, essentially making it "show its work." This is especially useful for complex or edge-case problems where the model might otherwise hallucinate or produce logically inconsistent results.

**Three proven techniques:**

### 1. Direct Step-by-Step Instructions

Add "Think step by step" to your prompt. This simple phrase triggers the model to break down complex problems into logical components rather than jumping to conclusions.

### 2. Provide a Logical Framework

Instead of hoping the model figures out the right approach, give it the exact framework to follow. This reduces reasoning errors by constraining the solution path.

**Example:**

*Vague prompt:*

```
Analyze the impact of climate change on polar bear populations.
```

*Structured prompt:*

```
Analyze the impact of climate change on polar bear populations using this framework:
1. Current polar bear population status
2. Climate change factors affecting Arctic habitat
3. Direct impacts (habitat loss, hunting changes)
4. Indirect impacts (food chain disruption)
5. Future population projections
```

### 3. XML Tags for Process Separation

Use `<thinking></thinking>` and `<answer></answer>` tags to separate reasoning from final output. This prevents the model from mixing its working process with the polished result, especially useful for complex multi-step problems. Learn more about XML tags [here](/en/using-langdock/prompt-engineering/advanced-techniques/advanced-principles/structure-prompt).

***

## Quick Comparison

| Technique            | Best For                                          | Complexity |
| -------------------- | ------------------------------------------------- | ---------- |
| **Zero-shot**        | General tasks, classification, translation        | Simple     |
| **Few-shot**         | Tasks requiring specific format or nuanced output | Medium     |
| **Chain-of-thought** | Complex reasoning, multi-step problems            | Advanced   |

***

## Combining Techniques

You can combine these techniques for even better results:

* Use **few-shot** examples with **chain-of-thought** instructions for complex reasoning tasks
* Apply **chain-of-thought** principles to **zero-shot** prompts when dealing with edge cases
* Structure **few-shot** examples using **chain-of-thought** frameworks for consistent reasoning