!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()||function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}())

[Skip to main content](#__docusaurus_skipToContent_fallback)

You are viewing the **preview** v0.2 docs. View the **stable** v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386).

[

![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png)

](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com)

[More](#)

*   [People](/v0.2/docs/people/)
*   [Community](/v0.2/docs/community)
*   [Tutorials](/v0.2/docs/additional_resources/tutorials)
*   [Contributing](/v0.2/docs/contributing)

[v0.2](#)

*   [v0.2](/v0.2/docs/introduction)
*   [v0.1](https://js.langchain.com/v0.1/docs/get_started/introduction)

[🦜🔗](#)

*   [LangSmith](https://smith.langchain.com)
*   [LangSmith Docs](https://docs.smith.langchain.com)
*   [LangChain Hub](https://smith.langchain.com/hub)
*   [LangServe](https://github.com/langchain-ai/langserve)
*   [Python Docs](https://python.langchain.com/)

[Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs)

Search

*   [Introduction](/v0.2/docs/introduction)
*   [Tutorials](/v0.2/docs/tutorials/)
    
    *   [Build a Question Answering application over a Graph Database](/v0.2/docs/tutorials/graph)
    *   [Tutorials](/v0.2/docs/tutorials/)
    *   [Build a Simple LLM Application](/v0.2/docs/tutorials/llm_chain)
    *   [Build a Query Analysis System](/v0.2/docs/tutorials/query_analysis)
    *   [Build a Chatbot](/v0.2/docs/tutorials/chatbot)
    *   [Build an Agent](/v0.2/docs/tutorials/agents)
    *   [Build an Extraction Chain](/v0.2/docs/tutorials/extraction)
    *   [Summarize Text](/v0.2/docs/tutorials/summarization)
    *   [Tagging](/v0.2/docs/tutorials/classification)
    *   [Build a Local RAG Application](/v0.2/docs/tutorials/local_rag)
    *   [Conversational RAG](/v0.2/docs/tutorials/qa_chat_history)
    *   [Build a Retrieval Augmented Generation (RAG) App](/v0.2/docs/tutorials/rag)
    *   [Build a Question/Answering system over SQL data](/v0.2/docs/tutorials/sql_qa)
*   [How-to guides](/v0.2/docs/how_to/)
    
    *   [How-to guides](/v0.2/docs/how_to/)
    *   [How to use example selectors](/v0.2/docs/how_to/example_selectors)
    *   [Installation](/v0.2/docs/how_to/installation)
    *   [How to stream responses from an LLM](/v0.2/docs/how_to/streaming_llm)
    *   [How to stream chat model responses](/v0.2/docs/how_to/chat_streaming)
    *   [How to embed text data](/v0.2/docs/how_to/embed_text)
    *   [How to use few shot examples in chat models](/v0.2/docs/how_to/few_shot_examples_chat)
    *   [How to cache model responses](/v0.2/docs/how_to/llm_caching)
    *   [How to cache chat model responses](/v0.2/docs/how_to/chat_model_caching)
    *   [How to create a custom LLM class](/v0.2/docs/how_to/custom_llm)
    *   [How to use few shot examples](/v0.2/docs/how_to/few_shot_examples)
    *   [How to use output parsers to parse an LLM response into structured format](/v0.2/docs/how_to/output_parser_structured)
    *   [How to return structured data from a model](/v0.2/docs/how_to/structured_output)
    *   [How to add ad-hoc tool calling capability to LLMs and Chat Models](/v0.2/docs/how_to/tools_prompting)
    *   [How to create a custom chat model class](/v0.2/docs/how_to/custom_chat)
    *   [How to do per-user retrieval](/v0.2/docs/how_to/qa_per_user)
    *   [How to track token usage](/v0.2/docs/how_to/chat_token_usage_tracking)
    *   [How to track token usage](/v0.2/docs/how_to/llm_token_usage_tracking)
    *   [How to pass through arguments from one step to the next](/v0.2/docs/how_to/passthrough)
    *   [How to compose prompts together](/v0.2/docs/how_to/prompts_composition)
    *   [How to use legacy LangChain Agents (AgentExecutor)](/v0.2/docs/how_to/agent_executor)
    *   [How to add values to a chain's state](/v0.2/docs/how_to/assign)
    *   [How to attach runtime arguments to a Runnable](/v0.2/docs/how_to/binding)
    *   [How to cache embedding results](/v0.2/docs/how_to/caching_embeddings)
    *   [How to split by character](/v0.2/docs/how_to/character_text_splitter)
    *   [How to manage memory](/v0.2/docs/how_to/chatbots_memory)
    *   [How to do retrieval](/v0.2/docs/how_to/chatbots_retrieval)
    *   [How to use tools](/v0.2/docs/how_to/chatbots_tools)
    *   [How to split code](/v0.2/docs/how_to/code_splitter)
    *   [How to do retrieval with contextual compression](/v0.2/docs/how_to/contextual_compression)
    *   [How to write a custom retriever class](/v0.2/docs/how_to/custom_retriever)
    *   [How to create custom Tools](/v0.2/docs/how_to/custom_tools)
    *   [How to debug your LLM apps](/v0.2/docs/how_to/debugging)
    *   [How to load CSV data](/v0.2/docs/how_to/document_loader_csv)
    *   [How to write a custom document loader](/v0.2/docs/how_to/document_loader_custom)
    *   [How to load data from a directory](/v0.2/docs/how_to/document_loader_directory)
    *   [How to load PDF files](/v0.2/docs/how_to/document_loader_pdf)
    *   [How to load JSON data](/v0.2/docs/how_to/document_loaders_json)
    *   [How to select examples by length](/v0.2/docs/how_to/example_selectors_length_based)
    *   [How to select examples by similarity](/v0.2/docs/how_to/example_selectors_similarity)
    *   [How to use reference examples](/v0.2/docs/how_to/extraction_examples)
    *   [How to handle long text](/v0.2/docs/how_to/extraction_long_text)
    *   [How to do extraction without using function calling](/v0.2/docs/how_to/extraction_parse)
    *   [Fallbacks](/v0.2/docs/how_to/fallbacks)
    *   [Few Shot Prompt Templates](/v0.2/docs/how_to/few_shot)
    *   [How to run custom functions](/v0.2/docs/how_to/functions)
    *   [How to construct knowledge graphs](/v0.2/docs/how_to/graph_constructing)
    *   [How to map values to a database](/v0.2/docs/how_to/graph_mapping)
    *   [How to improve results with prompting](/v0.2/docs/how_to/graph_prompting)
    *   [How to add a semantic layer over the database](/v0.2/docs/how_to/graph_semantic)
    *   [How to reindex data to keep your vectorstore in-sync with the underlying data source](/v0.2/docs/how_to/indexing)
    *   [How to get log probabilities](/v0.2/docs/how_to/logprobs)
    *   [How to add message history](/v0.2/docs/how_to/message_history)
    *   [How to generate multiple embeddings per document](/v0.2/docs/how_to/multi_vector)
    *   [How to generate multiple queries to retrieve data for](/v0.2/docs/how_to/multiple_queries)
    *   [How to parse JSON output](/v0.2/docs/how_to/output_parser_json)
    *   [How to retry when output parsing errors occur](/v0.2/docs/how_to/output_parser_retry)
    *   [How to parse XML output](/v0.2/docs/how_to/output_parser_xml)
    *   [How to invoke runnables in parallel](/v0.2/docs/how_to/parallel)
    *   [How to retrieve the whole document for a chunk](/v0.2/docs/how_to/parent_document_retriever)
    *   [How to partially format prompt templates](/v0.2/docs/how_to/prompts_partial)
    *   [How to add chat history to a question-answering chain](/v0.2/docs/how_to/qa_chat_history_how_to)
    *   [How to return citations](/v0.2/docs/how_to/qa_citations)
    *   [How to return sources](/v0.2/docs/how_to/qa_sources)
    *   [How to stream from a question-answering chain](/v0.2/docs/how_to/qa_streaming)
    *   [How to construct filters](/v0.2/docs/how_to/query_constructing_filters)
    *   [How to add examples to the prompt](/v0.2/docs/how_to/query_few_shot)
    *   [How to deal with high cardinality categorical variables](/v0.2/docs/how_to/query_high_cardinality)
    *   [How to handle multiple queries](/v0.2/docs/how_to/query_multiple_queries)
    *   [How to handle multiple retrievers](/v0.2/docs/how_to/query_multiple_retrievers)
    *   [How to handle cases where no queries are generated](/v0.2/docs/how_to/query_no_queries)
    *   [How to recursively split text by characters](/v0.2/docs/how_to/recursive_text_splitter)
    *   [How to reduce retrieval latency](/v0.2/docs/how_to/reduce_retrieval_latency)
    *   [How to route execution within a chain](/v0.2/docs/how_to/routing)
    *   [How to chain runnables](/v0.2/docs/how_to/sequence)
    *   [How to split text by tokens](/v0.2/docs/how_to/split_by_token)
    *   [How to deal with large databases](/v0.2/docs/how_to/sql_large_db)
    *   [How to use prompting to improve results](/v0.2/docs/how_to/sql_prompting)
    *   [How to do query validation](/v0.2/docs/how_to/sql_query_checking)
    *   [How to stream](/v0.2/docs/how_to/streaming)
    *   [How to create a time-weighted retriever](/v0.2/docs/how_to/time_weighted_vectorstore)
    *   [How to use a chat model to call tools](/v0.2/docs/how_to/tool_calling)
    *   [How to call tools with multi-modal data](/v0.2/docs/how_to/tool_calls_multi_modal)
    *   [How to use LangChain tools](/v0.2/docs/how_to/tools_builtin)
    *   [How use a vector store to retrieve data](/v0.2/docs/how_to/vectorstore_retriever)
    *   [How to create and query vector stores](/v0.2/docs/how_to/vectorstores)
*   [Conceptual guide](/v0.2/docs/concepts)
*   Ecosystem
    
    *   [🦜🛠️ LangSmith](/v0.2/docs/langsmith/)
        
    *   [🦜🕸️LangGraph.js](/v0.2/docs/langgraph)
*   Versions
    
    *   [Overview](/v0.2/docs/versions/overview)
    *   [v0.2](/v0.2/docs/versions/v0_2)
    *   [Release Policy](/v0.2/docs/versions/release_policy)
    *   [Packages](/v0.2/docs/versions/packages)
*   [Security](/v0.2/docs/security)

*   [](/v0.2/)
*   [How-to guides](/v0.2/docs/how_to/)
*   How to use few shot examples

On this page

How to use few shot examples
============================

In this guide, we’ll learn how to create a simple prompt template that provides the model with example inputs and outputs when generating. Providing the LLM with a few such examples is called few-shotting, and is a simple yet powerful way to guide generation and in some cases drastically improve model performance.

A few-shot prompt template can be constructed from either a set of examples, or from an [Example Selector](https://v02.api.js.langchain.com/classes/langchain_core_example_selectors.BaseExampleSelector.html) class responsible for choosing a subset of examples from the defined set.

This guide will cover few-shotting with string prompt templates. For a guide on few-shotting with chat messages for chat models, see [here](/v0.2/docs/how_to/few_shot_examples_chat/).

Prerequisites

This guide assumes familiarity with the following concepts:

*   [Prompt templates](/v0.2/docs/concepts/#prompt-templates)
*   [Example selectors](/v0.2/docs/concepts/#example-selectors)
*   [LLMs](/v0.2/docs/concepts/#llms)
*   [Vectorstores](/v0.2/docs/concepts/#vectorstores)

Create a formatter for the few-shot examples[​](#create-a-formatter-for-the-few-shot-examples "Direct link to Create a formatter for the few-shot examples")
------------------------------------------------------------------------------------------------------------------------------------------------------------

Configure a formatter that will format the few-shot examples into a string. This formatter should be a `PromptTemplate` object.

    import { PromptTemplate } from "@langchain/core/prompts";const examplePrompt = PromptTemplate.fromTemplate(  "Question: {question}\n{answer}");

Creating the example set[​](#creating-the-example-set "Direct link to Creating the example set")
------------------------------------------------------------------------------------------------

Next, we’ll create a list of few-shot examples. Each example should be a dictionary representing an example input to the formatter prompt we defined above.

    const examples = [  {    question: "Who lived longer, Muhammad Ali or Alan Turing?",    answer: `  Are follow up questions needed here: Yes.  Follow up: How old was Muhammad Ali when he died?  Intermediate answer: Muhammad Ali was 74 years old when he died.  Follow up: How old was Alan Turing when he died?  Intermediate answer: Alan Turing was 41 years old when he died.  So the final answer is: Muhammad Ali  `,  },  {    question: "When was the founder of craigslist born?",    answer: `  Are follow up questions needed here: Yes.  Follow up: Who was the founder of craigslist?  Intermediate answer: Craigslist was founded by Craig Newmark.  Follow up: When was Craig Newmark born?  Intermediate answer: Craig Newmark was born on December 6, 1952.  So the final answer is: December 6, 1952  `,  },  {    question: "Who was the maternal grandfather of George Washington?",    answer: `  Are follow up questions needed here: Yes.  Follow up: Who was the mother of George Washington?  Intermediate answer: The mother of George Washington was Mary Ball Washington.  Follow up: Who was the father of Mary Ball Washington?  Intermediate answer: The father of Mary Ball Washington was Joseph Ball.  So the final answer is: Joseph Ball  `,  },  {    question:      "Are both the directors of Jaws and Casino Royale from the same country?",    answer: `  Are follow up questions needed here: Yes.  Follow up: Who is the director of Jaws?  Intermediate Answer: The director of Jaws is Steven Spielberg.  Follow up: Where is Steven Spielberg from?  Intermediate Answer: The United States.  Follow up: Who is the director of Casino Royale?  Intermediate Answer: The director of Casino Royale is Martin Campbell.  Follow up: Where is Martin Campbell from?  Intermediate Answer: New Zealand.  So the final answer is: No  `,  },];

### Pass the examples and formatter to `FewShotPromptTemplate`[​](#pass-the-examples-and-formatter-to-fewshotprompttemplate "Direct link to pass-the-examples-and-formatter-to-fewshotprompttemplate")

Finally, create a [`FewShotPromptTemplate`](https://v02.api.js.langchain.com/classes/langchain_core_prompts.FewShotPromptTemplate.html) object. This object takes in the few-shot examples and the formatter for the few-shot examples. When this `FewShotPromptTemplate` is formatted, it formats the passed examples using the `examplePrompt`, then and adds them to the final prompt before `suffix`:

    import { FewShotPromptTemplate } from "@langchain/core/prompts";const prompt = new FewShotPromptTemplate({  examples,  examplePrompt,  suffix: "Question: {input}",  inputVariables: ["input"],});const formatted = await prompt.format({  input: "Who was the father of Mary Ball Washington?",});console.log(formatted.toString());

    Question: Who lived longer, Muhammad Ali or Alan Turing?  Are follow up questions needed here: Yes.  Follow up: How old was Muhammad Ali when he died?  Intermediate answer: Muhammad Ali was 74 years old when he died.  Follow up: How old was Alan Turing when he died?  Intermediate answer: Alan Turing was 41 years old when he died.  So the final answer is: Muhammad AliQuestion: When was the founder of craigslist born?  Are follow up questions needed here: Yes.  Follow up: Who was the founder of craigslist?  Intermediate answer: Craigslist was founded by Craig Newmark.  Follow up: When was Craig Newmark born?  Intermediate answer: Craig Newmark was born on December 6, 1952.  So the final answer is: December 6, 1952Question: Who was the maternal grandfather of George Washington?  Are follow up questions needed here: Yes.  Follow up: Who was the mother of George Washington?  Intermediate answer: The mother of George Washington was Mary Ball Washington.  Follow up: Who was the father of Mary Ball Washington?  Intermediate answer: The father of Mary Ball Washington was Joseph Ball.  So the final answer is: Joseph BallQuestion: Are both the directors of Jaws and Casino Royale from the same country?  Are follow up questions needed here: Yes.  Follow up: Who is the director of Jaws?  Intermediate Answer: The director of Jaws is Steven Spielberg.  Follow up: Where is Steven Spielberg from?  Intermediate Answer: The United States.  Follow up: Who is the director of Casino Royale?  Intermediate Answer: The director of Casino Royale is Martin Campbell.  Follow up: Where is Martin Campbell from?  Intermediate Answer: New Zealand.  So the final answer is: NoQuestion: Who was the father of Mary Ball Washington?

By providing the model with examples like this, we can guide the model to a better response.

Using an example selector[​](#using-an-example-selector "Direct link to Using an example selector")
---------------------------------------------------------------------------------------------------

We will reuse the example set and the formatter from the previous section. However, instead of feeding the examples directly into the `FewShotPromptTemplate` object, we will feed them into an implementation of `ExampleSelector` called [`SemanticSimilarityExampleSelector`](https://v02.api.js.langchain.com/classes/langchain_core_example_selectors.SemanticSimilarityExampleSelector.html) instance. This class selects few-shot examples from the initial set based on their similarity to the input. It uses an embedding model to compute the similarity between the input and the few-shot examples, as well as a vector store to perform the nearest neighbor search.

To show what it looks like, let’s initialize an instance and call it in isolation:

Set your OpenAI API key for the embeddings model

    export OPENAI_API_KEY="..."

    import { SemanticSimilarityExampleSelector } from "@langchain/core/example_selectors";import { MemoryVectorStore } from "langchain/vectorstores/memory";import { OpenAIEmbeddings } from "@langchain/openai";const exampleSelector = await SemanticSimilarityExampleSelector.fromExamples(  // This is the list of examples available to select from.  examples,  // This is the embedding class used to produce embeddings which are used to measure semantic similarity.  new OpenAIEmbeddings(),  // This is the VectorStore class that is used to store the embeddings and do a similarity search over.  MemoryVectorStore,  {    // This is the number of examples to produce.    k: 1,  });// Select the most similar example to the input.const question = "Who was the father of Mary Ball Washington?";const selectedExamples = await exampleSelector.selectExamples({ question });console.log(`Examples most similar to the input: ${question}`);for (const example of selectedExamples) {  console.log("\n");  console.log(    Object.entries(example)      .map(([k, v]) => `${k}: ${v}`)      .join("\n")  );}

    Examples most similar to the input: Who was the father of Mary Ball Washington?question: Who was the maternal grandfather of George Washington?answer:  Are follow up questions needed here: Yes.  Follow up: Who was the mother of George Washington?  Intermediate answer: The mother of George Washington was Mary Ball Washington.  Follow up: Who was the father of Mary Ball Washington?  Intermediate answer: The father of Mary Ball Washington was Joseph Ball.  So the final answer is: Joseph Ball

Now, let’s create a `FewShotPromptTemplate` object. This object takes in the example selector and the formatter prompt for the few-shot examples.

    const prompt = new FewShotPromptTemplate({  exampleSelector,  examplePrompt,  suffix: "Question: {input}",  inputVariables: ["input"],});const formatted = await prompt.invoke({  input: "Who was the father of Mary Ball Washington?",});console.log(formatted.toString());

    Question: Who was the maternal grandfather of George Washington?  Are follow up questions needed here: Yes.  Follow up: Who was the mother of George Washington?  Intermediate answer: The mother of George Washington was Mary Ball Washington.  Follow up: Who was the father of Mary Ball Washington?  Intermediate answer: The father of Mary Ball Washington was Joseph Ball.  So the final answer is: Joseph BallQuestion: Who was the father of Mary Ball Washington?

Next steps[​](#next-steps "Direct link to Next steps")
------------------------------------------------------

You’ve now learned how to add few-shot examples to your prompts.

Next, check out the other how-to guides on prompt templates in this section, the related how-to guide on [few shotting with chat models](/v0.2/docs/how_to/few_shot_examples_chat), or the other [example selector how-to guides](/v0.2/docs/how_to/example_selectors/).

* * *

#### Was this page helpful?

  

#### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E).

[

Previous

How to create a custom LLM class

](/v0.2/docs/how_to/custom_llm)[

Next

How to use output parsers to parse an LLM response into structured format

](/v0.2/docs/how_to/output_parser_structured)

*   [Create a formatter for the few-shot examples](#create-a-formatter-for-the-few-shot-examples)
*   [Creating the example set](#creating-the-example-set)
    *   [Pass the examples and formatter to `FewShotPromptTemplate`](#pass-the-examples-and-formatter-to-fewshotprompttemplate)
*   [Using an example selector](#using-an-example-selector)
*   [Next steps](#next-steps)

Community

*   [Discord](https://discord.gg/cU2adEyC7w)
*   [Twitter](https://twitter.com/LangChainAI)

GitHub

*   [Python](https://github.com/langchain-ai/langchain)
*   [JS/TS](https://github.com/langchain-ai/langchainjs)

More

*   [Homepage](https://langchain.com)
*   [Blog](https://blog.langchain.dev)

Copyright © 2024 LangChain, Inc.