A Beginner's Guide to Few-Shot Prompting in LangChain

Prompting is at the core of how we interact with language models. Whether you're generating structured outputs, handling user queries, or building agent workflows, how you frame the input shapes the quality of the response. In a previous article, we explored how LangChain’s PromptTemplate helps organize and reuse prompts more effectively. But what happens when a single example isn’t enough?

That’s where few-shot prompting comes in. Instead of just giving the model a task description, you show it a few examples of how the task should be done. This small change often leads to much better results, especially in tasks like formatting, classification, or style matching.

Few-shot prompting gives you more control without the overhead of fine-tuning. LangChain supports this pattern natively, making it easier to structure your examples, keep your prompts clean, and adapt to different tasks quickly.

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In this guide, you’ll learn what few-shot prompting is, how LangChain implements it, when to use it, and how to build your own effective prompts using real examples.

What is Few-Shot Prompting?

Few-shot prompting is a technique where you guide a language model by giving it a handful of examples before the actual input. Instead of just saying "Translate this sentence" or "Classify this review", you show the model how similar tasks have been done. That context helps the model generate better, more consistent results.

For example, say you're building a prompt for sentiment classification. A zero-shot prompt might look like:

Classify the sentiment of this review:
"This product was surprisingly good and arrived on time."

With a few-shot approach, you provide examples first:

Review: "Terrible customer service."
Sentiment: Negative

Review: "Amazing experience from start to finish."
Sentiment: Positive

Review: "This product was surprisingly good and arrived on time."
Sentiment:

The model uses the pattern in your examples to understand what you're asking for. It doesn’t just read the instructions, it learns from the examples you give it in the moment.

Few-shot prompting works especially well when:

• You want consistent output formatting
• The task is nuanced or vague without context
• You don’t have the time or data to fine-tune a model

It’s a lightweight, flexible way to improve model behavior without touching the model itself.

In the next section, we’ll look at how LangChain makes this easy to implement through its FewShotPromptTemplate.

How LangChain Supports Few-Shot Prompting

LangChain gives you a clean, structured way to build few-shot prompts using the FewShotPromptTemplate. Instead of manually stitching together examples, task descriptions, and user inputs, this utility lets you define a format and plug in examples programmatically.

Basically, FewShotPromptTemplate works by combining three pieces:

• A list of examples
• An example prompt template (how each example is formatted)
• A main prompt template (what wraps around the examples)

This setup makes your prompt easier to manage and scale, especially when working across different tasks or LLM chains.

Here’s a quick breakdown of each component:

1. examples

This is a list of your input-output pairs. Think of them as in-context demonstrations. For example:

const examples = [
  { question: "What is the capital of France?", answer: "Paris" },
  { question: "What is the capital of Japan?", answer: "Tokyo" },
];

2. examplePrompt

This defines how each example is formatted. You use a PromptTemplate here to keep things consistent:

const examplePrompt = PromptTemplate.fromTemplate(
  "Q: {question}\nA: {answer}"
);

3. FewShotPromptTemplate

Now you bring it all together:

const fewShotPrompt = new FewShotPromptTemplate({
  examples,
  examplePrompt,
  prefix: "Answer the following questions.",
  suffix: "Q: {input}\nA:",
  inputVariables: ["input"],
});

When you call this prompt with an actual question, LangChain will automatically insert the examples, wrap them with the prefix and suffix, and produce the final prompt ready for the model.

The result is clean, readable, and consistent, exactly what you want when prompting models that can be sensitive to small changes in input.

In the next section, we’ll walk through a full example to show how it all works in practice.

Step-by-Step Example

Let’s walk through a full working example using LangChain’s FewShotPromptTemplate. We'll build a simple prompt that helps a model convert plain text into structured JSON objects. This is a common task in real-world apps where you want clean, machine-readable outputs from user input.

Use Case: Converting Product Descriptions to JSON

We want to pass a few examples that show how to extract structured fields like name, category, and price from raw text descriptions.

1. Define the Examples

Each example represents the pattern we want the model to learn.

const examples = [
  {
    description: "The iPhone 14 is a sleek smartphone priced at $799.",
    output: '{ "name": "iPhone 14", "category": "smartphone", "price": 799 }'
  },
  {
    description: "MacBook Pro 16-inch is available now for $2499.",
    output: '{ "name": "MacBook Pro 16-inch", "category": "laptop", "price": 2499 }'
  }
];

2. Create the Example Prompt Template

This defines how each example should be formatted inside the final prompt.

const examplePrompt = PromptTemplate.fromTemplate(
  "Input: {description}\nOutput: {output}"
);

3. Set Up the Few-Shot Prompt

Now combine the examples with the overall task prompt.

const fewShotPrompt = new FewShotPromptTemplate({
  examples,
  examplePrompt,
  prefix: "Convert the following product descriptions to JSON.",
  suffix: "Input: {input}\nOutput:",
  inputVariables: ["input"]
});

4. Generate the Final Prompt

Now you can pass in a new input and get the full prompt ready for the model.

const formattedPrompt = await fewShotPrompt.format({
  input: "The Apple Watch Series 9 is priced at $399."
});

console.log(formattedPrompt);

Output:

Convert the following product descriptions to JSON.

Input: The iPhone 14 is a sleek smartphone priced at $799.
Output: { "name": "iPhone 14", "category": "smartphone", "price": 799 }

Input: MacBook Pro 16-inch is available now for $2499.
Output: { "name": "MacBook Pro 16-inch", "category": "laptop", "price": 2499 }

Input: The Apple Watch Series 9 is priced at $399.
Output:

This format gives the model a clear pattern to follow, improving accuracy and consistency without needing extra instructions.

In the next section, we’ll cover some practical best practices for using few-shot prompting effectively in your own projects.

Best Practices for Few-Shot Prompting

Few-shot prompting is powerful, but like most things in prompt engineering, small details can make a big difference. Here are some simple, practical tips to help you get the most out of it.

1. Use High-Quality, Focused Examples

Your examples should reflect the task you want the model to perform. Keep them short, clear, and relevant. If you're asking the model to extract structured data, show exactly how it should be done with accurate inputs and clean outputs. Avoid noise or edge cases unless you're specifically trying to teach the model how to handle them.

2. Be Consistent in Formatting

Models are sensitive to patterns. Inconsistent punctuation, spacing, or wording across your examples can lead to inconsistent results. Use the same structure for each example, and format your prompts the way you want the model to respond.

3. Put the Most Informative Examples First

If you're limited by token space, place the strongest or clearest examples at the top. Some models seem to weigh earlier examples more heavily, especially when prompt length starts to stretch.

4. Watch Token Limits

Few-shot prompting uses more tokens than zero-shot, and it adds up fast. If you’re working with a long prompt or large examples, keep an eye on token count to avoid getting cut off or truncated responses. Trim down where you can without losing clarity.

5. Keep Examples Close to the Input

Try not to separate your examples from the user input with too much extra text. Keeping everything tight and logically grouped helps the model maintain context and follow the pattern more easily.

6. Experiment with Example Order and Number

Sometimes, simply changing the order of examples improves the output. If results aren't consistent, test with fewer or more examples. There's no fixed rule—some tasks work well with two or three examples, while others may need more.

Few-shot prompting isn’t magic, but when done right, it feels like it. In the next section, we’ll talk about when this technique makes sense, and when it might not be the best fit.

When (and When Not) to Use Few-Shot Prompting

Few-shot prompting is a solid technique, but it’s not always the right tool for the job. Knowing when to use it, and when to consider other options, can save you time and help you get better results from your models.

When Few-Shot Prompting Works Well

• You need consistent output formatting:

  If you want the model to return structured text (like JSON, YAML, or bullet points), a few examples can guide it to follow the format reliably.

• The task requires a specific tone or style:

  Showing a few samples helps the model pick up on voice, tone, or phrasing. This is useful for writing product descriptions, support responses, or summaries.

• The task is subjective or nuanced:

  Few-shot prompts can help models make better judgments by showing how you expect them to reason through ambiguous cases.

• You're working without fine-tuning:

  If you don’t have custom model training set up, few-shot prompting is often the fastest way to improve results with minimal setup.

When It’s Not the Best Fit

• Your inputs are very long:

  If the user input is large (like a document or a long transcript), adding examples might push you over the model’s context limit. In those cases, consider summarizing or chunking the input first.

• You need deep reasoning or tool use:

  Complex reasoning tasks may need more than pattern matching. LangChain tools, agents, or RAG (retrieval-augmented generation) might be better suited.

• The task is very simple or well-known to the model:

  For basic Q&A, math problems, or direct instructions, few-shot prompting might not add much and could even slow things down.

The key is to match the technique to the task. If you’re unsure, test both zero-shot and few-shot approaches side by side and compare the outputs.

Few-shot prompting is one of the simplest and most effective ways to guide language models toward better results. With just a few well-crafted examples, you can shape outputs, control tone, and handle tasks that would otherwise require fine-tuning.

LangChain makes this process easier through its FewShotPromptTemplate, letting you build reusable, clean prompts that scale well across use cases. Whether you're formatting responses, doing classification, or extracting structured data, few-shot prompting can give you the extra precision you're looking for.

Start small. Test with real examples from your application. Adjust as needed. Like most things in prompt engineering, iteration is where the value shows up.

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Thanks for reading, and happy prompting.