Basic Prompt Engineering

Welcome to this hands-on guide on prompt engineering using the OpenAI Python client. You’ll learn how to install the package, configure the client, build a reusable prompt function, and tune generation parameters like max_tokens, temperature, top_p, and stop.

Table of Contents

1. Prerequisites
2. Installation
3. Client Setup
4. Creating the Prompt Function
5. Running and Testing
6. Tuning Generation Parameters
   • max_tokens
   • temperature
   • top_p
   • stop
7. Parameter Reference Table
8. Summary
9. Links and References

Prerequisites

• Python 3.7+
• An OpenAI API key
• Basic familiarity with Python

Installation

Open your terminal in Visual Studio Code (Terminal → New Terminal) and install the OpenAI package:

pip3 install openai

You should see output indicating successful installation:

Requirement already satisfied: tqdm<4 in ./Library/Python/3.9/lib/python/site-packages (from openai) (4.66.5)
Requirement already satisfied: anyio<6,>=5.0.0 in ./Library/Python/3.9/lib/python/site-packages (from openai) (5.4.0)
Requirement already satisfied: httpx<1.23.0,>=0.23.0 in ./Library/Python/3.9/lib/python/site-packages (from openai) (0.27.2)
...

Clear the terminal before proceeding.

Client Setup

Create a new file named prompt_engine.py and initialize the OpenAI client. For this example, we’ll inject the API key inline—remember to switch to environment variables later.

from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY")

Creating the Prompt Function

Define a function prompt_engine that sends user input to the model and returns the generated text:

from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY")

def prompt_engine(prompt: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

Running and Testing

Append a sample prompt and print the result:

prompt = "You are an NBA basketball expert. Who's better, MJ or LeBron?"
print(prompt_engine(prompt))

Then run:

python3 prompt_engine.py

You’ll see the model’s comparison between Michael Jordan and LeBron James.

Tuning Generation Parameters

Fine-tuning parameters lets you control creativity, length, and focus. Here’s how to adjust the main options:

max_tokens

Controls the maximum number of tokens in the response. Increase for more detailed output:

def prompt_engine(prompt: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}],
        max_tokens=200
    )
    return response.choices[0].message.content

temperature

Sets randomness:

• 0.0 for deterministic responses
• 1.0 for highly creative output

def prompt_engine(prompt: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.5
    )
    return response.choices[0].message.content

top_p

Limits token selection to a cumulative probability. Lower values focus the output:

def prompt_engine(prompt: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.5,
        top_p=0.5
    )
    return response.choices[0].message.content

stop

Define one or more stop sequences to end the generation early:

def prompt_engine(prompt: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}],
        max_tokens=100,
        temperature=0.5,
        top_p=0.5,
        stop=["\n"]
    )
    return response.choices[0].message.content

Parameter Reference Table

Summary

You’ve now covered:

• Installing the OpenAI Python SDK
• Initializing the OpenAI client
• Writing a generic prompt_engine function
• Running and validating outputs
• Fine-tuning with max_tokens, temperature, top_p, and stop

Experiment with these settings to craft prompts that deliver exactly the style and length you need.

Links and References

• OpenAI Python SDK Documentation
• Chat Completions API Reference
• Python Packaging Guide