{"id":8046,"date":"2023-11-01T04:57:23","date_gmt":"2023-11-01T12:57:23","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=8046"},"modified":"2025-04-24T17:05:02","modified_gmt":"2025-04-24T17:05:02","slug":"introduction-to-prompt-templates-in-langchain","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/introduction-to-prompt-templates-in-langchain\/","title":{"rendered":"Introduction to Prompt Templates in LangChain"},"content":{"rendered":"\n<section class=\"section section--body\">\n<div class=\"section-content\">\n<div class=\"section-inner sectionLayout--insetColumn\">\n<h4 class=\"graf graf--h4\">A Deep Dive into Structured Language Model Interactions<\/h4>\n<figure class=\"graf graf--figure\"><img decoding=\"async\" class=\"graf-image\" src=\"https:\/\/cdn-images-1.medium.com\/max\/1600\/0*8yyEfyqbjYPOBpXz\" data-image-id=\"0*8yyEfyqbjYPOBpXz\" data-width=\"5184\" data-height=\"3456\" data-unsplash-photo-id=\"Fa9b57hffnM\" data-is-featured=\"true\"><figcaption class=\"imageCaption\">Photo by <a class=\"markup--anchor markup--figure-anchor\" href=\"https:\/\/unsplash.com\/@sigmund?utm_source=medium&amp;utm_medium=referral\" target=\"_blank\" rel=\"photo-creator noopener\" data-href=\"https:\/\/unsplash.com\/@sigmund?utm_source=medium&amp;utm_medium=referral\">Sigmund<\/a> on&nbsp;<a class=\"markup--anchor markup--figure-anchor\" href=\"https:\/\/unsplash.com?utm_source=medium&amp;utm_medium=referral\" target=\"_blank\" rel=\"photo-source noopener\" data-href=\"https:\/\/unsplash.com?utm_source=medium&amp;utm_medium=referral\">Unsplash<\/a><\/figcaption><\/figure>\n<p class=\"graf graf--p\">Language models have rapidly evolved to become a cornerstone of many AI-driven applications.<\/p>\n<p class=\"graf graf--p\">However, their power is rooted in their advanced architectures and their ability to effectively interpret and respond to user prompts. In this context, LangChain introduces a game-changing tool: <strong><code>PromptTemplates<\/code><\/strong>.<\/p>\n<p class=\"graf graf--p\">At first glance, one might perceive a prompt as a simple question or request.<\/p>\n<p class=\"graf graf--p\">Yet, in Language Models, prompts are the bridge that connects human intent to machine-generated responses. They guide the model, providing context, refining outputs, and modifying behaviours. And while crafting the perfect prompt might seem straightforward, the reality is that it\u2019s both an art and a science.<\/p>\n<p class=\"graf graf--p\">Enter <strong><code class=\"markup--code markup--p-code\">PromptTemplates<\/code><\/strong> in LangChain.<\/p>\n<p class=\"graf graf--p\">These aren\u2019t just about sending a question to a model. They offer a structured, reusable, and dynamic way to interact with various language models. From setting the context and defining instructions to dynamically adjusting the content based on user needs, <strong><code class=\"markup--code markup--p-code\">PromptTemplates<\/code><\/strong> offers a versatile approach to language model interactions.<\/p>\n<p class=\"graf graf--p\">This guide will take you through the intricacies of <code class=\"markup--code markup--p-code\"><strong>PromptTemplates<\/strong><\/code> in LangChain, illuminating their significance, functionality, and the benefits they bring to the table. Whether you\u2019re new to language models or a seasoned pro, understanding <code class=\"markup--code markup--p-code\"><strong>PromptTemplates<\/strong><\/code> is paramount to harnessing the full potential of LangChain and the models it interacts with.<\/p>\n<h3 class=\"graf graf--h3\">Prompts<\/h3>\n<p class=\"graf graf--p\"><strong class=\"markup--strong markup--p-strong\">Language models (LLMs)<\/strong> require prompts to function.<\/p>\n<p class=\"graf graf--p\">A prompt is a set of instructions or inputs to guide the model\u2019s response. The output from a prompt can be answers, sentence completions, or conversation responses. A well-constructed prompt template has the following sections:<\/p>\n<ul class=\"postList\">\n<li class=\"graf graf--li\"><strong class=\"markup--strong markup--li-strong\">Instructions<\/strong>: Define the model\u2019s response\/behaviour.<\/li>\n<li class=\"graf graf--li\"><strong class=\"markup--strong markup--li-strong\">Context<\/strong>: Provides additional information, sometimes with examples.<\/li>\n<li class=\"graf graf--li\"><strong class=\"markup--strong markup--li-strong\">User Input<\/strong>: The actual question or input from the user.<\/li>\n<li class=\"graf graf--li\"><strong class=\"markup--strong markup--li-strong\">Output Indicator<\/strong>: Marks the beginning of the model\u2019s response.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/section>\n\n\n\n<section class=\"section section--body\">\n<div class=\"section-divider\">\n<hr class=\"section-divider\">\n<\/div>\n<div class=\"section-content\">\n<div class=\"section-inner sectionLayout--insetColumn\">\n<blockquote class=\"graf graf--pullquote\"><p>Want to learn how to build modern software with LLMs using the newest tools and techniques in the field? <a class=\"markup--anchor markup--pullquote-anchor\" href=\"https:\/\/www.comet.com\/production\/site\/llm-course\/?utm_source=Heartbeat&amp;utm_medium=referral&amp;utm_content=Medium\" target=\"_blank\" rel=\"noopener ugc nofollow\" data-href=\"https:\/\/www.comet.com\/production\/site\/llm-course\/?utm_source=Heartbeat&amp;utm_medium=referral&amp;utm_content=Medium\">Check out this free LLMOps course<\/a> from industry expert Elvis Saravia of DAIR.AI, in collaboration with Comet.<\/p><\/blockquote>\n<\/div>\n<\/div>\n<\/section>\n\n\n\n<section class=\"section section--body\">\n<div class=\"section-divider\">\n<hr class=\"section-divider\">\n<\/div>\n<div class=\"section-content\">\n<div class=\"section-inner sectionLayout--insetColumn\">\n<h3 class=\"graf graf--h3\">What is a Prompt Template?<\/h3>\n<p class=\"graf graf--p\">Generating, sharing, and reusing prompts in a reproducible manner can be achieved using a few key components.<\/p>\n<p class=\"graf graf--p\">These include a text string or template that takes inputs and produces a prompt for the LLM, instructions to train the LLM, few-shot examples to enhance the model\u2019s response, and a question to guide the language model.<\/p>\n<p class=\"graf graf--p\">These pre-defined recipes can contain instructions, context, few-shot examples, and questions that are appropriate for a particular task.<\/p>\n<p class=\"graf graf--p\">LangChain offers a set of tools for creating and working with prompt templates. These templates are designed to be model-agnostic, making them easier to reuse across different language models. Language models generally require prompts to be in the form of a string or a list of chat messages.<\/p>\n<p class=\"graf graf--p\">Why Use Prompt Templates? Prompt templates are useful when multiple inputs are needed, making code cleaner and more manageable.<\/p>\n<h3 class=\"graf graf--h3\">Prompt templates in LangChain<\/h3>\n<p class=\"graf graf--p\">LangChain provides <strong><code class=\"markup--code markup--p-code\">PromptTemplate<\/code><\/strong> to help create parametrized prompts for language models.<\/p>\n<p class=\"graf graf--p\">A <strong><code class=\"markup--code markup--p-code\">PromptTemplate<\/code><\/strong> allows creating a template string with placeholders, like <strong><code class=\"markup--code markup--p-code\">{adjective}<\/code><\/strong> or <strong><code class=\"markup--code markup--p-code\">{content}<\/code><\/strong> that can be formatted with input values to create the final prompt string.<\/p>\n<p class=\"graf graf--p\">Some key features:<\/p>\n<ul class=\"postList\">\n<li class=\"graf graf--li\">Validation of input variables against the template<\/li>\n<li class=\"graf graf--li\">Flexible input values\u200a\u2014\u200acan pass dictionaries, data classes, etc<\/li>\n<li class=\"graf graf--li\">Support for different templating engines like Python\u2019s `str.format` or Jinja2<\/li>\n<li class=\"graf graf--li\">Easy to extend and create custom templates<\/li>\n<\/ul>\n<pre class=\"graf graf--pre graf--preV2\" spellcheck=\"false\" data-code-block-mode=\"1\" data-code-block-lang=\"python\"><span class=\"pre--content\"><span class=\"hljs-keyword\">from<\/span> langchain <span class=\"hljs-keyword\">import<\/span> PromptTemplate, OpenAI\n\n<span class=\"hljs-comment\"># Define a simple prompt template as a Python string<\/span>\n\nprompt_template = PromptTemplate.from_template(<span class=\"hljs-string\">\"\"\"\nHuman: What is the capital of {place}?\nAI: The capital of {place} is {capital}\n\"\"\"<\/span>)\n\nprompt = prompt_template.<span class=\"hljs-built_in\">format<\/span>(place=<span class=\"hljs-string\">\"California\"<\/span>, capital=<span class=\"hljs-string\">\"Sacramento\"<\/span>)\n\n<span class=\"hljs-built_in\">print<\/span>(prompt)<\/span><\/pre>\n<p class=\"graf graf--p\">This will show the prompt as:<\/p>\n<pre class=\"graf graf--pre graf--preV2\" spellcheck=\"false\" data-code-block-mode=\"2\" data-code-block-lang=\"plaintext\"><span class=\"pre--content\">Human: What is the capital of California?\nAI: The capital of California is Sacramento<\/span><\/pre>\n<p class=\"graf graf--p\">You can take this prompt and pass it to an LLM:<\/p>\n<pre class=\"graf graf--pre graf--preV2\" spellcheck=\"false\" data-code-block-mode=\"2\" data-code-block-lang=\"python\"><span class=\"pre--content\">prompt_template = PromptTemplate.from_template(\n    template=<span class=\"hljs-string\">\"Write a {length} story about: {content}\"<\/span>\n)\n\nllm = OpenAI()\n\nprompt = prompt_template.<span class=\"hljs-built_in\">format<\/span>(\n    length=<span class=\"hljs-string\">\"2-sentence\"<\/span>,\n    content=<span class=\"hljs-string\">\"The hometown of the legendary data scientist, Harpreet Sahota\"<\/span>\n)\n\nresponse = llm.predict(\n    text=prompt\n)\n\n<span class=\"hljs-built_in\">print<\/span>(response)<\/span><\/pre>\n<p class=\"graf graf--p\">Which outputs the following, almost true, tale of Harpreet Sahota:<\/p>\n<pre class=\"graf graf--pre graf--preV2\" spellcheck=\"false\" data-code-block-mode=\"2\" data-code-block-lang=\"plaintext\"><span class=\"pre--content\">Harpreet Sahota's small hometown was always proud of him, even before he became a household name as the legendary data scientist. His intelligence and dedication to the field has earned him recognition around the world.<\/span><\/pre>\n<p class=\"graf graf--p\">You can instantiate a prompt template with no input variables, one input variable, or multiple input variables, like so:<\/p>\n<pre class=\"graf graf--pre graf--preV2\" spellcheck=\"false\" data-code-block-mode=\"1\" data-code-block-lang=\"python\"><span class=\"pre--content\"><span class=\"hljs-comment\"># No Input Variable<\/span>\nno_input_prompt = PromptTemplate(input_variables=[], template=<span class=\"hljs-string\">\"Tell me a joke.\"<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(no_input_prompt.<span class=\"hljs-built_in\">format<\/span>())\n\n<span class=\"hljs-comment\"># One Input Variable<\/span>\none_input_prompt = PromptTemplate(input_variables=[<span class=\"hljs-string\">\"adjective\"<\/span>], template=<span class=\"hljs-string\">\"Tell me a {adjective} joke.\"<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(one_input_prompt.<span class=\"hljs-built_in\">format<\/span>(adjective=<span class=\"hljs-string\">\"funny\"<\/span>))\n\n<span class=\"hljs-comment\"># Multiple Input Variables<\/span>\nmultiple_input_prompt = PromptTemplate(\n input_variables=[<span class=\"hljs-string\">\"adjective\"<\/span>, <span class=\"hljs-string\">\"content\"<\/span>],\n template=<span class=\"hljs-string\">\"Tell me a {adjective} joke about {content}.\"<\/span>\n)\n\nmultiple_input_prompt = multiple_input_prompt.<span class=\"hljs-built_in\">format<\/span>(adjective=<span class=\"hljs-string\">\"funny\"<\/span>, content=<span class=\"hljs-string\">\"chickens\"<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(multiple_input_prompt)<\/span><\/pre>\n<p class=\"graf graf--p\">Which will output the following:<\/p>\n<pre class=\"graf graf--pre graf--preV2\" spellcheck=\"false\" data-code-block-mode=\"2\" data-code-block-lang=\"plaintext\"><span class=\"pre--content\">Tell me a joke.\nTell me a funny joke.\nTell me a funny joke about chickens.<\/span><\/pre>\n<p class=\"graf graf--p\">And pass this to an LLM like so:<\/p>\n<pre class=\"graf graf--pre graf--preV2\" spellcheck=\"false\" data-code-block-mode=\"2\" data-code-block-lang=\"python\"><span class=\"pre--content\">response = llm.predict(\n    text=multiple_input_prompt\n)\n\n<span class=\"hljs-built_in\">print<\/span>(response)<\/span><\/pre>\n<pre class=\"graf graf--pre graf--preV2\" spellcheck=\"false\" data-code-block-mode=\"2\" data-code-block-lang=\"plaintext\"><span class=\"pre--content\">Q: What did the chicken do when he saw an earthquake?\nA: He egg-scaped!<\/span><\/pre>\n<h3 class=\"graf graf--h3\">Why would I even use a prompt template?<\/h3>\n<p class=\"graf graf--p\">Here are some practical use cases for using a prompt template rather than passing a plain prompt to a language model:<\/p>\n<h4 class=\"graf graf--h4\">Reusability<\/h4>\n<p class=\"graf graf--p\">Prompt templates allow you to define a template once and reuse it in multiple places. This avoids duplicating the same prompt logic over and over. For example, you could create a \u201csummarize article\u201d template and reuse it anytime you want a summary.<\/p>\n<h4 class=\"graf graf--h4\">Separation of&nbsp;concerns<\/h4>\n<p class=\"graf graf--p\">Prompt templates separate the prompt formatting from the model invocation. This makes the code more modular\u200a\u2014\u200ayou can change the template or the model independently.<\/p>\n<h4 class=\"graf graf--h4\">Dynamic prompts<\/h4>\n<p class=\"graf graf--p\">Templates allow you to dynamically generate prompts by filling in template variables. This is useful when you want to customize the prompt based on user input or other runtime factors.<\/p>\n<h4 class=\"graf graf--h4\">Readability<\/h4>\n<p class=\"graf graf--p\">Templates can improve readability by encapsulating complex prompt logic in a simple interface. Named variables are often clearer than trying to embed logic directly in strings.<\/p>\n<h4 class=\"graf graf--h4\">Maintenance<\/h4>\n<p class=\"graf graf--p\">Changes to shared prompt logic only need to happen in one place rather than everywhere a prompt is defined. This improves maintainability.<\/p>\n<p class=\"graf graf--p\">So in summary, prompt templates improve reusability, modularity and maintenance of prompt engineering code compared to using raw prompt strings directly.<\/p>\n<h3 class=\"graf graf--h3\">Chat prompt templates<\/h3>\n<p class=\"graf graf--p\">For chat models, LangChain provides <strong><code class=\"markup--code markup--p-code\">ChatPromptTemplate<\/code><\/strong> which allows creating a template for a list of chat messages.<\/p>\n<p class=\"graf graf--p\">You can use the provided chat message classes like <strong><code class=\"markup--code markup--p-code\">AIMessage<\/code><\/strong>, <code class=\"markup--code markup--p-code\"><strong>HumanMessage<\/strong><\/code>, etc or plain tuples to define the chat messages.<\/p>\n<p class=\"graf graf--p\"><code class=\"markup--code markup--p-code\"><strong>ChatPromptTemplate<\/strong><\/code> allows formatting the messages with input values to create the final list of chat messages.<\/p>\n<pre class=\"graf graf--pre graf--preV2\" spellcheck=\"false\" data-code-block-mode=\"1\" data-code-block-lang=\"python\"><span class=\"pre--content\"><span class=\"hljs-keyword\">from<\/span> langchain.prompts <span class=\"hljs-keyword\">import<\/span> ChatPromptTemplate\n\nchat_template = ChatPromptTemplate.from_messages([\n    (<span class=\"hljs-string\">\"human\"<\/span>, <span class=\"hljs-string\">\"What is the capital of {country}?\"<\/span>),\n    (<span class=\"hljs-string\">\"ai\"<\/span>, <span class=\"hljs-string\">\"The capital of {country} is {capital}.\"<\/span>)\n])\n\nmessages = chat_template.format_messages(\n    country=<span class=\"hljs-string\">\"Canada\"<\/span>,\n    capital=<span class=\"hljs-string\">\"Winnipeg\"<\/span>\n)\n\n<span class=\"hljs-built_in\">print<\/span>(messages)<\/span><\/pre>\n<p class=\"graf graf--p\">Which will output the following:<\/p>\n<pre class=\"graf graf--pre graf--preV2\" spellcheck=\"false\" data-code-block-mode=\"2\" data-code-block-lang=\"plaintext\"><span class=\"pre--content\">[HumanMessage(content='What is the capital of Canada?', additional_kwargs={}, example=False), AIMessage(content='The capital of Canada is Winnipeg.', additional_kwargs={}, example=False)]<\/span><\/pre>\n<h3 class=\"graf graf--h3\">Conclusion<\/h3>\n<p class=\"graf graf--p\">Throughout our exploration of <strong><code class=\"markup--code markup--p-code\">PromptTemplates<\/code><\/strong> in LangChain, one thing becomes undeniably clear: the true power of a language model isn&#8217;t just in its underlying architecture but in how we communicate with it.<\/p>\n<p class=\"graf graf--p\"><strong><code class=\"markup--code markup--p-code\">PromptTemplates<\/code><\/strong> are not merely tools; they are the refined language through which we converse with sophisticated AI systems, ensuring precision, clarity, and adaptability in every interaction.<\/p>\n<p class=\"graf graf--p\">LangChain\u2019s introduction of such a structured approach to prompts marks a significant step forward in the AI domain.<\/p>\n<p class=\"graf graf--p\">By emphasizing reusability, dynamism, and modularity, LangChain ensures that developers can maximize the efficacy of their language model interactions without getting bogged down by complexities.<\/p>\n<p class=\"graf graf--p\">As we move forward in this AI-driven era, tools like <code class=\"markup--code markup--p-code\"><strong>PromptTemplates<\/strong><\/code> will undoubtedly play a pivotal role in defining the boundaries of what&#8217;s possible. They stand as a testament to the fact that, while the evolution of AI is essential, the methods we employ to interact with it are equally crucial.<\/p>\n<p class=\"graf graf--p\">With LangChain and <strong><code class=\"markup--code markup--p-code\">PromptTemplates<\/code><\/strong> at our disposal, the future of seamless, impactful, and meaningful AI interactions looks incredibly bright.<\/p>\n<\/div>\n<\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>A Deep Dive into Structured Language Model Interactions Photo by Sigmund on&nbsp;Unsplash Language models have rapidly evolved to become a cornerstone of many AI-driven applications. However, their power is rooted in their advanced architectures and their ability to effectively interpret and respond to user prompts. In this context, LangChain introduces a game-changing tool: PromptTemplates. At [&hellip;]<\/p>\n","protected":false},"author":68,"featured_media":9443,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"customer_name":"","customer_description":"","customer_industry":"","customer_technologies":"","customer_logo":"","footnotes":""},"categories":[65,7],"tags":[70,71,52,31,33,34],"coauthors":[166],"class_list":["post-8046","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-llmops","category-tutorials","tag-langchain","tag-language-models","tag-llm","tag-llmops","tag-openai","tag-prompt-engineering"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Introduction to Prompt Templates in LangChain - Comet<\/title>\n<meta name=\"description\" content=\"In language model interactions, prompt templates set the context, define instructions, and dynamically adjust the 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