Jinja2 is Awesome for AI Prompt Templates

7 May 202406:59

TLDRLearntail is a tool that generates quizzes from various sources, and the developers have found Jinja2 to be an excellent templating engine for creating prompts for AI. They started with simple text prompts, moved to YAML files, and then to LangChain, before settling on Jinja2 for its flexibility and control. The video introduces a simple function to interact with the OpenAI API using Jinja2 templates, which can be customized with tags for dynamic content. An example is provided where an email is written for a customer about a product being out of stock, with options to suggest alternatives and personalize the tone. The video also demonstrates a helper function to process templates and a main file that uses these components to create and send an email template. The presenter invites viewers to share their prompt management strategies and hints at further videos exploring more advanced Jinja2 capabilities.


  • 🚀 **Jinja2 for Templating**: The speaker discusses using Jinja2, a templating engine, for creating AI prompt templates which is more controlled and customizable than simple text-based prompts.
  • 📚 **YAML to Jinja2 Transition**: Initially, YAML files were used, but they were replaced with Jinja2 for better design and functionality.
  • 🤖 **LangChain Experience**: The team experimented with LangChain but found it insufficient in terms of design for their needs.
  • 📝 **Jinja2 with OpenAI API**: Jinja2 is used in conjunction with a basic setup for interaction with the OpenAI API for the Learntail tool.
  • 🔌 **Simple API Wrapper**: A simple Python wrapper was created to simplify interactions with the OpenAI API, making it easier to use.
  • 📦 **Models and Types**: The script mentions defining models like GPT-3 and GPT-4, and introduces the new type syntax for Python 3.12.
  • 💬 **Chat Interface via Closure**: A closure is used to create a simple chat interface that can send requests to the AI, leveraging the scope of the function.
  • 📬 **Email Template Example**: An example of an email template is given, showcasing how Jinja2 can be used to inject specific values into a template, such as product names and CEO signatures.
  • 🛠️ **Jinja2 Template Features**: Jinja2's capabilities extend beyond simple replacements, including if-else statements and more, which can be used for complex prompts.
  • 🔧 **Helper Function**: A helper function named `processTemplate` is introduced to load and render Jinja2 templates with given data.
  • 🔑 **API Key Integration**: The script demonstrates how to use an API key with the `chatter` function to interact with the OpenAI API.
  • ✍️ **Dynamic Email Content**: The process of creating an email with dynamic content, tone, and alternatives is shown, highlighting the flexibility of Jinja2 templates.

Q & A

  • What is Learntail and how does it generate quizzes?

    -Learntail is a tool that can generate quizzes from various sources such as text, websites, and even YouTube videos. It uses a combination of simple text-based prompts, YAML files, and Jinja2 templating engine to create these quizzes.

  • Why did the developers choose Jinja2 for Learntail?

    -The developers chose Jinja2 because it is a very capable templating engine that can be combined with a basic setup for interacting with the OpenAI API, which they use for Learntail. It is typically used for generating HTML but is also suitable for tools that need to interact with a language model (LLM).

  • What is the purpose of the 'chatter' function in the script?

    -The 'chatter' function is a simple wrapper around the OpenAI API that creates a chat interface. It uses a closure to send a chat request and returns the chat result from the response, simplifying the interaction with the AI.

  • How does the Jinja2 templating engine help in formulating prompts?

    -Jinja2 templating engine helps in formulating prompts by allowing the use of tags that can be replaced by actual values. This enables the creation of dynamic and personalized prompts, such as emails, which can be customized for different customers or situations.

  • What is the 'processTemplate' function used for?

    -The 'processTemplate' function is a helper function that takes a template file and data to be passed to the template. It interacts with the Jinja2 package to load the file, render the template with the provided data, and return the result.

  • How does the script demonstrate the use of Jinja2 templates?

    -The script demonstrates the use of Jinja2 templates by showing how to create an email template for a specific customer regarding a product being out of stock. It shows how to inject variables such as the product name, a similar product suggestion, tone of voice, and the CEO's name into the template.

  • What is the significance of using a closure in the 'chatter' function?

    -Using a closure in the 'chatter' function allows the function to retain access to the variables in its lexical scope, which in this case includes the OpenAI client. This enables the closure to send a chat request using the client, encapsulating the complexity of the operation.

  • Why is the 'Better Jinja' extension mentioned in the script?

    -The 'Better Jinja' extension is mentioned because it provides syntax highlighting for Jinja2, which can be helpful for developers when creating and editing Jinja templates.

  • What are some of the other capabilities of Jinja2 that can be used to construct more complicated prompts?

    -Jinja2 has capabilities like if-else statements, loops, filters, and more, which can be incredibly helpful in constructing more complicated and dynamic prompts.

  • What are some considerations when using OpenAI's API in a project?

    -Some considerations when using OpenAI's API in a project include managing API keys securely, handling the complexity of the API interactions, and potentially integrating additional functionalities like JSON parsing and database storage.

  • How can one try out Learntail for free?

    -To try out Learntail for free, one can visit the website learntail.com as mentioned in the script.

  • What is the potential for future content based on the script?

    -The potential for future content includes a more in-depth video on Jinja2, exploring additional functionalities and integrations, and possibly discussing other tools and methods for managing prompts and LLM interactions.



🚀 Introduction to Learntail and Template Development

The first paragraph introduces Learntail, a tool designed to generate quizzes from various sources like text, websites, and YouTube videos. The creator discusses their journey in finding the best way to work with prompts, starting from simple text-based prompts to using more complex methods like YAML files and LangChain, before settling on Jinja2. Jinja2 is highlighted as a versatile templating engine suitable for interacting with large language models (LLMs). The speaker provides a link to the full code in a Git repository and hints at a potential future video on Jinja2. A simple function for interacting with the OpenAI API is demonstrated, showcasing the use of closures to simplify the process of sending chat requests. The paragraph concludes with an overview of using Jinja2 templates to formulate prompts, emphasizing the customization and personalization capabilities for creating content like emails.


📧 Automating Email Creation with Jinja2 Templates

The second paragraph delves into the process of creating an email template using Jinja2, which is then utilized with a helper function called processTemplate. This function takes a template file and data to render a customized result. The paragraph outlines the steps to get an API key for OpenAI, use the chatter function with the chosen model (GPT-3 in this case), and input data such as customer name, product details, and tone of voice. An example of an email for a product out of stock is given, demonstrating how easily the template can be adjusted for different scenarios. The speaker also discusses the possibility of changing the tone and receiving a response incorporating emojis, showcasing the flexibility of the template system. The paragraph ends with a call to action for viewers to share their methods for managing prompts and LLM interactions and a teaser for a follow-up video discussing considerations when using OpenAI's API in projects.




Learntail is a tool mentioned in the video that is capable of generating quizzes from various sources such as text, websites, and YouTube videos. It represents the main application where the discussed technologies and processes are implemented. In the context of the video, Learntail utilizes Jinja2 templates and interacts with the OpenAI API to create dynamic and personalized content for quizzes.


Jinja2 is a templating engine for Python that is used in the video to create dynamic content. It is traditionally used for generating HTML, but as explained, it's also suitable for applications that require interaction with a language model (LLM). In the video, Jinja2 is highlighted for its capability to create templates that can be filled with specific data, such as product information in an email template.

💡YAML files

YAML, which stands for 'YAML Ain't Markup Language,' is a data serialization format used in the video's context for its initial approach to creating prompts. YAML files are structured and human-readable, making them a common choice for configuration files. However, the video suggests that YAML files were eventually replaced by Jinja2 for more complex and controlled prompt generation.


LangChain is mentioned as a tool that was experimented with for prompt management but was not found to be satisfactory in terms of design for the creators' needs. It implies that LangChain is one of the many tools available for working with language models but may not be the best fit for every project or team.


The OpenAI API is a service that powers the language model interactions in the video. It is used by Learntail to generate content dynamically. The video discusses creating a simple wrapper function around the OpenAI API to facilitate its use within the application, emphasizing its importance in the workflow.

💡GPT models

GPT-3 and GPT-4 are specific models of language generators provided by OpenAI. These models are referenced in the video as options that can be utilized within the chat interface created for Learntail. The models are selected based on the requirements of the task at hand, showcasing the flexibility of using different models for various applications.

💡templating engine

A templating engine like Jinja2 is a technology that allows for the insertion of dynamic content into pre-defined templates. In the context of the video, it is used to create customized prompts and messages for interaction with the OpenAI API. Templating engines are crucial for generating personalized and context-specific content at scale.

💡functional programming

Functional programming is a programming paradigm that is briefly mentioned in the context of using closures to create a simple and elegant solution for interacting with the OpenAI API. The video showcases how a function can return another function, a concept central to functional programming, to encapsulate and simplify complex operations.

💡Better Jinja

Better Jinja is an extension for Jinja2 that the video suggests installing for enhanced functionality. Specifically, it offers syntax highlighting for Jinja2 templates, which can improve the readability and ease of use for developers working with Jinja2 templates.

💡processTemplate function

The processTemplate function is a helper function introduced in the video to facilitate the use of Jinja2 templates. It takes a template file and data to be rendered within the template, interacts with the Jinja2 package to perform the rendering, and returns the result. This function is a practical example of how to integrate Jinja2 into a project for dynamic content generation.

💡API key

An API key is a unique identifier used to authenticate requests to an API, such as the OpenAI API. In the video, obtaining an API key is a prerequisite step before making calls to the OpenAI API. It ensures that the requests are coming from a valid and authenticated source, which is a common practice for securing API interactions.


Learntail is a tool that generates quizzes from text, websites, and YouTube videos.

Jinja2 is used as a templating engine for creating prompts for AI interactions.

A simple wrapper around the OpenAI API simplifies the interaction process.

Jinja2 templates can be customized with tags for dynamic content insertion.

The 'chatter' function is a closure that facilitates easy interaction with the AI.

Python 3.12's new type syntax is utilized for defining models and types.

The 'processTemplate' function renders Jinja2 templates with provided data.

Better Jinja is an extension that offers syntax highlighting for Jinja2.

An example email template is created for notifying a customer about a product being out of stock.

The email template can suggest a similar product and adjust the tone of voice.

The template system allows for easy swapping of elements like the CEO's name.

Jinja2 supports advanced features like if-else statements for complex prompt construction.

The 'create unavailable email' function demonstrates the process of generating an email using a template.

The use of templates enables quick changes to the content, such as the tone of voice.

Emojis and a co-pilot suggestion are examples of the customization possible with Jinja2 templates.

The video discusses considerations for using OpenAI's API in a project.

The Git repository contains the full code for the Jinja2 and OpenAI API integration.

Viewers are encouraged to share their methods for managing prompts and LLM interactions in the comments.

The video is an introductory guide and may have a more in-depth follow-up in the future.