Large language models (LLMs) like ChatGPT and Google Gemini are incredibly powerful. So powerful that many people only scratch the surface of what they can provide. By leveraging artificial intelligence (AI) and machine learning (ML), a large language model can compose complex prose, handle customer service queries, write code, and even tell a funny joke if a user asks. But an LLM model’s response will always be limited by user input. If you don’t give an LLM model a good input prompt, it’s not going to generate a particularly valuable output.
So what do you do about this? Some developers are turning to LangChain prompt templates, which are guides that help users generate queries for LLMs. Here’s an overview of the LangChain framework and examples of how you can use LangChain prompt engineering to get more use out of large language models.
What is a prompt template?
A prompt template is a reusable structure used to create language model prompts. It provides a consistent, reproducible way to formulate prompts, and this improves your chances of receiving high-quality outputs from language and chat models.
Prompt templates typically include placeholders or input variables that you can modify with specific information or context relevant to the task at hand. This allows you to create prompts dynamically, tailoring parameters to different scenarios or use cases.
LangChain prompt templates
LangChain is an open-source framework designed to simplify the development of applications powered by large language models (LLMs) like ChatGPT, Claude, Google Gemini, and Perplexity AI. It helps developers build complex, AI-driven workflows, and it supports tasks such as natural language understanding, question answering, and summarization.
A LangChain prompt template is a specific type of reusable template for LLM applications that use the LangChain framework. In LangChain, a prompt template is one where you can define a prompt structure with placeholders and then fill in those placeholders with dynamic content to generate the final complete prompt. You can see how this works in the following example, where a template parameter is adjusted to help an LLM chatbot that performs ecommerce customer service:
ecommerce_prompt_template = PromptTemplate(
input_variables=["customer_query"],
template="""
You are an AI chatbot specializing in ecommerce customer service.
Your goal is to assist customers with their inquiries related to products, orders, shipping, and returns.
Please respond to the following customer query in a helpful and friendly manner:
Customer Query: {customer_query}
"""
)
As you can see from the above example, prompt templates in LangChain can be written with syntax that humans understand, as opposed to needing the user to input precise computer code. That’s because LangChain works with Python, a high-level programming language known for its simplicity, readability, and flexibility. Python’s clean syntax makes it beginner-friendly yet powerful enough for advanced developers. And because it works with Python, LangChain offers a lower barrier to entry.
Here’s another LangChain prompt that could be used for upselling products to a user who’s already browsing an ecommerce website:
upselling_prompt_template = PromptTemplate(
input_variables=["product_name", "customer_interest"],
template="""
You are an AI sales assistant for an ecommerce store. Your goal is to provide personalized product recommendations by suggesting complementary or premium products based on a customer’s interest. Make the recommendation friendly, persuasive, and focused on value.
Customer Interest: {customer_interest}
Product They Are Considering: {product_name}
Suggest one or two complementary products and explain why they pair well with the product the customer is considering.
"""
)
Here’s one more LangChain prompt template, designed to provide customer service related to shipping and delivery:
shipping_prompt_template = PromptTemplate(
input_variables=["customer_location", "shipping_method"],
template="""
You are an AI assistant helping customers track and estimate shipping times for their ecommerce orders.
Customer’s Location: {customer_location}
Selected Shipping Method: {shipping_method}
Based on this information, provide an estimated delivery time, any potential delays, and a brief reassurance about the store’s commitment to timely delivery.
"""
)
Just because it’s easy to create LangChain Python code doesn’t mean that your code has to be simple. LangChain can understand prompts with multiple placeholders, conditional logic, and other features that help you create more sophisticated and customizable prompts.
Why use prompt templates?
When you use a format method for creating LLM prompts, you gain several advantages related to precision, flexibility, and generating useful outputs. Here are four reasons to consider using templates in your own LLM work:
Consistency across outputs
If you work in fields that involve customer support, content creation, or question answering, you know that clarity and precision are mandatory. Prompt templates make a lot of sense for these applications, because the templates ensure uniformity in the way instructions are presented to language models. The net result is more reliable and consistent responses.
Efficiency
Instead of rewriting instructions, templates allow you to focus on tweaking variables to suit the specific context, while the core components remain the same. The LLM will answer a question based on how you instruct it to function in the initial template programming. Its answers will only shift in response to the variable inputs you define during the initial template creation process.
Improved model performance
Well-designed templates can guide the model to better understand the desired task, reducing ambiguity and improving the quality of responses—and steering you away from the dreaded error message. For enhanced relevance of outputs, create templates that include examples or clear structures.
Flexibility and customization
Prompt templates are often formatted with placeholders for dynamic variables, allowing easy customization for various use cases. For example, a single template could handle multiple products or scenarios by simply swapping in different variable values. This makes the template model ideal for scalable applications.
Drawbacks of LangChain prompt templates
- Limited awareness of context
- Difficulties when user inputs don’t fit the template
- Complex chains can produce error-filled responses
LangChain prompt templates can be immensely useful for ecommerce site owners, but they lack the nimble understanding of human customer service representatives. Here are four potential drawbacks to using LangChain prompt templates on your site:
Limited awareness of context
LangChain prompt templates aren’t designed to remember prior user queries, which means that they can’t hold a back-and-forth conversation with the real human on the other end of the exchange. In other words, there’s no real context for each LangChain response. Every query is like starting from scratch.
Difficulties when user inputs don’t fit the template
Compared to other forms of generative AI, LangChain models don’t respond properly when a user’s input doesn’t fit a preset prompt template. If you create a LangChain prompt template for order tracking and the user’s query is about the product itself, the AI may be unable to respond coherently. More sophisticated chatbots can pivot based on what the user has typed.
Complex chains can produce error-filled responses
As the complexity of LangChain applications increases, you may find it hard to manage and maintain multiple prompt templates. You can partially counter this by organizing prompt templates into modular components, which simplifies complex responses. Still, the more inputs that the LangChain processes, the greater the chance there is for error.
How to create prompt templates in LangChain
To properly create a LangChain prompt template, you’ll need to factor in three main components: The first is your template string (or string template), which is the main guts of the prompt. The second component consists of your variables, which you’ll code with placeholders but will vary based on text input from the user. The third component is the parameters, and these are the actual values that your users will input. For instance, if you were writing a prompt template that converted US dollars to euros, your parameters might consist of specific dollar amounts, which then get fed into a formula.
With these core components in mind, here’s how you’d go about creating a prompt template in LangChain:
Define your template string
Your string template should include placeholders for dynamic variables. Consider using Python’s f-strings to make it easy to substitute values during runtime. (An f-string is a concise and intuitive way to embed variables directly into strings in Python.) Here’s an example:
Name = “Mark” job = “customer service”
Formatted_string = f"My name is {name}, and I am a {job} specialist.”
Output: My name is Mark, and I am a customer service specialist.
Create your prompt template
You’ll instruct the program to “import PromptTemplate.” In this step, you’ll define your prompt, your input variables, and the template itself. This will formalize the final string you’ll use in LLM calls.
Incorporate few-shot examples
To improve performance, consider adding few-shot examples to the prefix of your prompt. This means providing the AI with a few examples of how to generate a desired response. These examples train the model using few-shot learning, demonstrating the expected output format or style. For example, you could train the language model to reply to a shipping query with the following example:
Query: “Where is my order?”
Response: “Your order {order number} departed our warehouse on {departure date} and is due to arrive on {arrival date}.”
Add external information capabilities (optional)
Add variables for dynamic external information like data from APIs or databases to make your prompts more adaptable. You can do this by including placeholders in the template string.
Test by generating a prompt
Software developers would say that in this stage, you “print“ your output. You do this by substituting variables and running the template to make sure it can pass muster. If it does, you’re ready to integrate it into your LLM calls.
LangChain prompt template FAQ
What can LangChain prompt templates be used for?
LangChain prompt templates can be used to create flexible and reusable prompts for various language model tasks, such as text generation, translation, summarization, and question answering. You can always adjust the context of your prompt to get better outputs.
What is prompt engineering?
Prompt engineering is the process of designing and optimizing inputs (prompts) for large language models to achieve specific, accurate, and relevant outputs.
What are the components of prompts?
Prompt components typically include instructions, examples, and specific task requirements, providing the language model with the necessary context to generate accurate and relevant responses.
