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prompt engineering

Creating Dynamic Prompts with Jinja2 for LLM Queries

When you're dealing with Large Language Models (LLMs) like ChatGPT from OpenAI, dynamically generating prompts based on different use-cases becomes essential.

Welcome to another tutorial at datascience.fm! Today, we're diving into the world of templating with Python, specifically using the powerful Jinja2 library. Why? Because when you're dealing with Large Language Models (LLMs) like ChatGPT from OpenAI, dynamically generating prompts based on different use-cases becomes essential.

1. Introduction to Jinja2

Jinja2 is a modern and designer-friendly templating engine for Python programming. It's primarily used for rendering dynamic web pages, but its applications extend far beyond, as we'll see today.

2. Setting up Your Environment

To kickstart our tutorial, we'll first need to install Jinja2. This can be done using Python's package manager pip:

pip install jinja2

3. Basic Jinja2 Syntax

Before diving into our specific use-case, let's get acquainted with Jinja2's syntax:

  • Variables: {{ variable_name }}
  • Statements: {% statement %}
  • Comments: {# comment #}

Variables are placeholders for dynamic content, statements are for logic (like loops or conditionals), and comments are, well, comments!

4. Constructing a Dynamic Prompt for LLMs

Now, to the heart of our tutorial: using the provided template to construct prompts. Let's break down the steps.

Step 1: Set up the template.

from jinja2 import Template

DEFAULT_KEYWORD_EXTRACT_TEMPLATE_TMPL = Template(
    "Some text is provided below. Given the text, extract up to {{ max_keywords }} "
    "keywords from the text. Avoid stopwords.\n"
    "---------------------\n"
    "{{ text }}\n"
    "---------------------\n"
    "Provide keywords in the following comma-separated format: 'KEYWORDS: <keywords>'\n"
)

Step 2: Provide dynamic content to the template.

def generate_prompt(text, max_keywords=5):
    return DEFAULT_KEYWORD_EXTRACT_TEMPLATE_TMPL.render(text=text, max_keywords=max_keywords)

Step 3: Use the function.

prompt = generate_prompt("Jinja2 is a popular templating engine in the Python ecosystem.", 3)
print(prompt)

The output would be a dynamic prompt asking the LLM to extract 3 keywords from the provided text.

5. Why Use Jinja2 for LLMs?

  1. Flexibility: Jinja2 allows for easy customization, which means you can adapt your prompts to various LLMs, each having potentially different requirements.
  2. Consistency: Templates ensure consistency in prompt structures, essential when making numerous queries to LLMs.
  3. Scalability: As your requirements grow, you can effortlessly add more templates or further customize existing ones.

Conclusion

Incorporating Jinja2 into your workflow when working with LLMs can greatly streamline the process, ensuring dynamic, consistent, and efficient prompts. While our example focused on keyword extraction, the principles can be extended to virtually any scenario. So go ahead, play around with Jinja2, and let it elevate your LLM querying game!

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