> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Demo Implementing Word Completion > This article teaches how to use the OpenAI ChatCompletion API in Jupyter Notebook for generating product descriptions and HTML snippets. 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](https://pypi.org/project/openai/) * [Jupyter Notebook](https://jupyter.org/) * 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. ```python theme={null} 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 = "" ``` Never commit your API key to source control. Use environment variables or a secrets manager to keep credentials safe. *** ## 2. Define the `get_word_completion` Helper Function Encapsulate the ChatCompletion call in a function to simplify reuse: ```python theme={null} 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 | Parameter | Purpose | Example | | ----------- | --------------------------------------- | --------------- | | model | Specifies which OpenAI model to use | `gpt-3.5-turbo` | | messages | Conversation roles & content array | system + user | | max\_tokens | Max tokens in the generated response | `3000` | | temperature | Controls randomness (0 = deterministic) | `1.2` | | n | Number of completions to generate | `1` | Adjust `temperature` to control creativity; lower values yield more focused outputs, higher values produce varied text. *** ## 3. Prepare the Product Specification We'll use a multi-line string as our product spec for a fictional **TerrainMaster Pro 2023 Mountain Bike**: ```python theme={null} 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. ````python theme={null} 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 `

` with the product name * A `
` 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 ````python theme={null} prompt = f""" Your task is to help the marketing team create HTML for a product website based on the product spec. - Add a

header with the product name. - Wrap the description in a
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)) ```` ![The image shows a Jupyter Notebook interface displaying a text block that defines the context for a prompt about the "TerrainMaster Pro 2023 Mountain Bike," detailing its features, specifications, and price.](https://kodekloud.com/kk-media/image/upload/v1752881538/notes-assets/images/Mastering-Generative-AI-with-OpenAI-Demo-Implementing-Word-Completion/jupyter-notebook-terrainmaster-pro-bike.jpg) *** You’ve now implemented a flexible word completion demo! Next up, we’ll explore GPT-3.5 Turbo’s code-completion capabilities.