Building First Prompt Templates with OpenAI Language Models

Introduction

In this blog post, we will explore how to build your first prompt and prompt templates using OpenAI Language Models. Language models are powerful tools that can generate text based on the input provided to them. By creating prompts and prompt templates, we can guide the language models to generate specific types of text output.

Getting Started

To begin, we need to set up OpenAI and obtain an API key. Firstly, visit the OpenAI website and log in to your account. Once logged in, navigate to the API section in your dashboard. Here, you can create a new API key by clicking on “Create new Secret key”. Remember to save your API key as you won’t be able to retrieve it later.

Setting Up Google Colab

To code our prompts and prompt templates, we will be using Google Colab. Go to colab.research.google.com and create a new notebook. Rename the notebook to your preference. Install the required libraries by running the following commands:

!pip install openai
!pip install langchain

the libraries are installed, import the necessary modules:

import os
from langchain.llms import OpenAI

os.environment["OPENAI_API_KEY"] = "sk*****************************"
from langchain.lms import openai

Creating Your First Prompt

Now that we have set up the necessary tools, let’s create our first prompt. Start by defining the desired temperature for the output text. The temperature determines the randomness of the generated text. Higher temperature values result in more random outputs, while lower values produce more deterministic results. as of now we will set the lower temperature.

temperature = 0.01

Next, we will set up our language model (LLM) using the OpenAI API and the specified temperature:

LLM = OpenAI(temperature=temperature)

Now, let’s create a specific prompt. For this example, let’s generate a baseball team name suitable for MLB expansion. We can use the following prompt:

prompt = "Create a baseball team name that would be great for MLB expansion."

To generate the text output, we can use the LM.predict() function:

output = LLM(prompt)
print(output)

# The “Rising Suns”

The output will be a baseball team name provided by the language model. You can run this code multiple times to get different team name suggestions.

Creating Prompt Templates

Prompt templates allow us to reuse prompts with different input variables. Let’s create a prompt template for generating baseball team names based on a specific city and primary color. Here’s an example:

from tempfile import template
from langchain.prompts import PromptTemplate 

llm = OpenAI(temperature = 0.2)
prompt_template_baseball_team = PromptTemplate(
    input_variables = ["city"],
    template = "Create a baseball team name that would be great for MLB expansion in {City} with primary color {Color}."
)
print(prompt_template_baseball_team.format(city = "New York", color = "Black"))

We can then use this template to generate team names by providing different values for the City and Color variables:

city = "Orlando"color = "red"prompt = template.format(City=city, Color=color)output = llm(prompt)

Saving and Loading Prompts

If you want to save your prompts for later use, you can do so by exporting them as JSON files. Here’s an example:

By changing the values of the City and Color variables, we can generate different team names.

prompt_template_baseball_team("baseball_prompt.json")

To load a prompt from a JSON file, you can use the load_prompt() function:

from langchain.prompts import load_prompts
loaded_prompt = load_prompt("baseball_prompt.json")
print(llm(loaded_prompt.format(city = "Delhi", color = "Brown")))

This allows you to easily reuse your saved prompts in future projects.

Creating Prompt Functions

If you find yourself needing to generate prompts frequently, it might be useful to create a function for it. Here’s an example of how to create a function to generate baseball team names:

from langchain.chains import LLMChain
def generate_baseball_team_name(city, color):
    llm = OpenAI(temperature = 0.03)
    prompt_template_baseball_team = PromptTemplate(
          input_variables = ["city"],
          template = "Create a baseball team name that would be great for MLB expansion in {City} with primary color {Color}."
)
    baseballchain = LLMChain(llm = llm, prompt = prompt_template_baseball_team
    response = baseballchain({'city': city, 'color': color})    return output

Now you can simply call the function with the desired city and color, and it will return a generated team name:

team_name = generate_baseball_team_name("Orlando", "blue")print(team_name)

Conclusion

In this blog post, we have learned how to build prompts and prompt templates using OpenAI Language Models. By utilizing these tools, we can guide the language models to generate specific types of text output. Whether you are creating baseball team names or any other type of text, prompts and prompt templates provide a powerful way to interact with language models.

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