Practical Applications

Explore real-world use cases for text generation with OpenAI’s GPT-4. Each example demonstrates how prompt engineering and model parameters influence the output. We cover:

• Blog post creation
• Summarization
• Conversational agents for customer support
• Code explanations and comments
• Creative writing
• Language translation

All snippets use the Chat Completion API for clarity and reproducibility.

Overview Table

1. Blog Post Generation

Generate full articles or individual sections by providing a clear prompt.

import openai

def generate_blog_intro():
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{
            "role": "user",
            "content": "Write a blog post introduction about the benefits of remote work for companies and employees."
        }],
        max_tokens=150,
        temperature=0.7
    )
    return response.choices[0].message.content

print(generate_blog_intro())

2. Text Summarization

Condense research papers, articles, or reports into concise summaries:

import openai

def summarize_article(article_text):
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{
            "role": "user",
            "content": f"Summarize this article on blockchain technology:\n\n{article_text}"
        }],
        max_tokens=100,
        temperature=0.3
    )
    return response.choices[0].message.content

article_text = (
    "Blockchain technology is a decentralized digital ledger that records "
    "transactions across many computers to ensure security and transparency."
)

print(summarize_article(article_text))

3. Conversational Agent for Customer Support

Build a polite, professional support agent that handles common inquiries:

import openai

def customer_support_response():
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[
            {"role": "system",  "content": "You are a helpful customer support agent."},
            {"role": "user",    "content": "A customer requests a refund for a defective product. Draft a professional response."}
        ],
        max_tokens=100,
        temperature=0.3
    )
    return response.choices[0].message.content

print(customer_support_response())

4. Code Explanations and Comments

Automatically generate inline comments or detailed explanations for any code snippet:

import openai

def explain_code(code_snippet):
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{
            "role": "user",
            "content": f"Explain the following Python function:\n\n{code_snippet}"
        }],
        max_tokens=150,
        temperature=0.3
    )
    return response.choices[0].message.content

code_snippet = '''
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)
'''

print(explain_code(code_snippet))

5. Creative Writing

Use GPT-4 to craft short stories, poems, or dialogues. Increase temperature for more imaginative output:

import openai

def short_story():
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{
            "role": "user",
            "content": "Write a short story about a robot learning to love music."
        }],
        max_tokens=200,
        temperature=0.9
    )
    return response.choices[0].message.content

print(short_story())

6. Language Translation

Translate text between languages by specifying the source and target:

import openai

def translate_text():
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{
            "role": "user",
            "content": "Translate this sentence from English to French: 'I hope you have a wonderful day.'"
        }],
        max_tokens=60
    )
    return response.choices[0].message.content

print(translate_text())

References

• OpenAI Chat Completion API
• OpenAI Models
• API Best Practices