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.

Prerequisites
Installation
Client Setup
Creating the Prompt Function
Running and Testing
Tuning Generation Parameters
- max_tokens
- temperature
- top_p
- stop
Parameter Reference Table
Summary
Links and References

Prerequisites

Python 3.7+
An OpenAI API key
Basic familiarity with Python

Never commit your API key directly to source control. Use environment variables or a secrets manager in production.

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

The image shows a code editor with a Python script open, displaying a function definition and a pop-up with parameter suggestions. The terminal at the bottom indicates a command-line interface.

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

top_p must be between 0 and 1 (exclusive). Values closer to 0 yield more focused results.

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

Parameter	Description	Example Values
model	ID of the OpenAI model or deployment	`"gpt-4o-mini"`
max_tokens	Maximum response length (in tokens)	`50`, `100`, `200`
temperature	Sampling temperature (0.0–1.0)	`0.0`, `0.5`, `1.0`
top_p	Nucleus sampling probability (0–1)	`0.1`, `0.5`, `1.0`
stop	Sequences where generation will stop	`["\n"]`, `["."]`

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.

Pre-Requisites

Introduction to AI

Text Generation

Features

Vision

Basic Prompt Engineering

Table of Contents

Prerequisites

Installation

Client Setup

Creating the Prompt Function

Running and Testing

Tuning Generation Parameters

max_tokens

temperature

top_p

stop

Parameter Reference Table

Summary

Links and References

Watch Video

Practice Lab

​Table of Contents

​Prerequisites

​Installation

​Client Setup

​Creating the Prompt Function

​Running and Testing

​Tuning Generation Parameters

​max_tokens

​temperature

​top_p

​stop

​Parameter Reference Table

​Summary

​Links and References

Watch Video

Practice Lab

Table of Contents

Prerequisites

Installation

Client Setup

Creating the Prompt Function

Running and Testing

Tuning Generation Parameters

max_tokens

temperature

top_p

stop

Parameter Reference Table

Summary

Links and References