Demo Implementing Word Completion

In this hands-on tutorial, you'll learn how to leverage the OpenAI ChatCompletion API inside a Jupyter Notebook to generate product descriptions, concise summaries, and HTML snippets. By the end, you'll have a reusable Python helper function and three prompt patterns for marketing automation.

Prerequisites

• Python 3.x
• openai Python package
• Jupyter Notebook
• An OpenAI API key

1. Setup: Import Modules & Configure API Key

Begin by importing the required libraries and setting your API key as an environment variable.

import os
import openai

# Securely load your API key; do NOT hardcode in notebooks shared publicly
openai.api_key = os.getenv("OPENAI_API_KEY")
# Uncomment the line below if you prefer to paste your key directly:
# openai.api_key = "<YOUR_API_KEY>"

2. Define the get_word_completion Helper Function

Encapsulate the ChatCompletion call in a function to simplify reuse:

def get_word_completion(prompt: str) -> str:
    """
    Sends a list of messages to the ChatCompletion endpoint 
    and returns the assistant's content.
    """
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user",   "content": prompt}
    ]
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=messages,
        max_tokens=3000,
        temperature=1.2,
        n=1
    )
    return response.choices[0].message.content

ChatCompletion.create Parameters

3. Prepare the Product Specification

We'll use a multi-line string as our product spec for a fictional TerrainMaster Pro 2023 Mountain Bike:

product_spec = """
Product Name:  TerrainMaster Pro 2023 Mountain Bike
Product Description:  The TerrainMaster Pro 2023 is an advanced mountain bike designed for the most demanding off-road conditions.

Key Features:
- Frame: Aerospace-grade aluminum frame for maximum strength and minimal weight.
- Suspension: Dual-suspension system with 180mm travel and hydraulic lockout.
- Gears: Shimano 24-speed drivetrain.
- Brakes: Hydraulic disc brakes.
- Tires: 29-inch, wide-profile, puncture-resistant tires.
- Seat: Ergonomic saddle with gel padding.

Additional Features:
- Handlebars: Wide, flat handlebars for improved control.
- Pedals: Alloy platform pedals with sealed bearings.
- Water Bottle Holder: Built-in for convenient hydration.
- Mudguards: Optional detachable mudguards.

Specifications:
- Frame Size: Small (15"), Medium (17"), Large (19"), X-Large (21").
- Wheel Size: 29 inches.
- Suspension: Full (front and rear).
- Brake Type: Hydraulic Disc.
- Frame Material: Aerospace-grade Aluminum.
- Weight: 13.5 kg (varies with frame size).

Usage Recommendations: Ideal for advanced riders on challenging off-road terrain.
Safety Precautions: Always wear protective gear and inspect components before riding.
Warranty: Limited lifetime frame warranty; 2-year warranty on other components.
Price: $900
"""

4. Example 1: Generating a Full Product Description

Use the full specification to generate a detailed website description.

prompt = f"""
Your task is to help the marketing team create a description 
for a product website based on the product spec.

Write a description using the information provided 
in the specifications delimited by triple backticks.

Technical specifications:
```{product_spec}```
"""

response = get_word_completion(prompt)
print(response)
```text

---

## 5. Example 2: Creating a Concise Version

To craft a shorter pitch for a landing page, simply instruct the model to be concise.

```python
prompt = f"""
Your task is to help the marketing team create a 
description for a product website based on the product spec.

Write a concise description from the specifications 
delimited by triple backticks.

Technical specifications:
```{product_spec}```
"""

response = get_word_completion(prompt)
print(response)

6. Example 3: Generating HTML with a Specifications Table

Ask GPT-3.5 Turbo to output a complete HTML snippet, including:

• An <h1> with the product name
• A <div> styled in 12pt FireBrick font for the description
• A titled table (Product Specifications) with two columns: name & value
• Table CSS: DarkSlateGray text, 100% width, 12pt font

prompt = f"""
Your task is to help the marketing team create 
HTML for a product website based on the product spec.

- Add a <h1> header with the product name.
- Wrap the description in a <div> styled with font-size:12pt; color:FireBrick.
- Include a 'Product Specifications' table:
  - Two columns: specification name | value
  - Table style: color DarkSlateGray; width 100%; font-size 12pt

Product specifications:
```{product_spec}```
"""

response = get_word_completion(prompt)
print(response)
```text

---

## 7. Previewing HTML in Jupyter Notebook

Render the generated HTML inline using `IPython.display`:

```python
from IPython.display import display, HTML

display(HTML(response))

You’ve now implemented a flexible word completion demo! Next up, we’ll explore GPT-3.5 Turbo’s code-completion capabilities.