AI Tool

ChatGPT

Overview

ChatGPT is a conversational AI (generative AI) developed by OpenAI, based on GPT models. Through natural language dialogue, it can perform a wide range of tasks including Q&A, writing, programming assistance, and data analysis. With the latest GPT-4o model, it enables integrated processing of text, images, and audio, demonstrating exceptional capabilities in programming learning and code explanation.

Details

Since its release in November 2022, ChatGPT has established itself as the most recognized AI tool and is widely used in programming assistance. The 2024 version has evolved from a traditional chat-based AI to a comprehensive development support platform by introducing innovative features such as integrated multimodal processing via GPT-4o, Advanced Data Analysis (formerly Code Interpreter), and real-time capabilities.

Latest Features (GPT-4o, Advanced Data Analysis)

• GPT-4o Integration: 2x processing speed improvement, 50% cost reduction compared to previous versions
• Multimodal: Simultaneous processing of text, images, and audio for intuitive operation
• Advanced Data Analysis: Immediate Python code execution and result display
• Real-time Support: Low-latency interactions
• Canvas Feature: Dedicated interface for code editing
• Memory Function: Learning individual user development patterns and preferences

Programming Support Specialized Features

• Code Generation: Generate executable code from natural language instructions
• Code Review: Problem identification and improvement suggestions for existing code
• Debug Assistance: Error message analysis and fix method suggestions
• Algorithm Explanation: Step-by-step explanations of complex algorithms
• Technical Learning: Beginner-friendly explanations of programming concepts

Advantages and Disadvantages

Advantages

• High Versatility: Wide support for programming languages, frameworks, and tools
• Superior Code Quality: Consistently generates implementable and maintainable code
• Learning Support: Technical learning promotion through step-by-step explanations and multiple solution approaches
• API Integration: Easy integration into existing systems with automation capabilities
• Multimodal: Integrated processing capabilities for code, diagrams, and documentation
• Real-time Response: Efficient development through immediate feedback and modification suggestions

Disadvantages

• Usage Limitations: Daily usage limits in free version (paid version recommended)
• Latest Information Gaps: Delayed support for latest technologies due to training data cutoff
• API Usage Costs: Token-based pricing for heavy usage (especially GPT-4o)
• Accuracy Variability: Possibility of misunderstanding or incomplete answers for complex requests
• AI Dependency Risk: Reduced learning opportunities due to over-reliance on AI-generated code

Reference Links

• ChatGPT Official Site
• OpenAI API Documentation
• OpenAI Cookbook
• GPT-4o Technical Specifications

Code Examples

Basic API Usage (Python)

from openai import OpenAI
import os

# Initialize OpenAI client
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))

# Basic code generation request
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a programming assistant. Generate executable, high-quality code."},
        {"role": "user", "content": "Write Python code for file reading and CSV processing"}
    ],
    temperature=0.1,  # Emphasize consistency
    max_tokens=1000
)

print(response.choices[0].message.content)

Streaming Response Dialogue

# Receive responses in real-time
stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "user", "content": "Explain custom hooks using React Hooks step by step with code"}
    ],
    stream=True,
    temperature=0.2
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

Prompt Engineering Optimization

# System prompt for high-quality code generation
system_prompt = """
You are a senior programmer. Generate code following these guidelines:

1. **Executability**: Provide only working code
2. **Error Handling**: Include proper exception handling
3. **Comments**: Write clear, understandable comments
4. **Readability**: Use maintainable structure and naming
5. **Security**: Follow security best practices
6. **Testing**: Provide test cases when possible

Additionally, format your response with:
- Code explanation
- Usage instructions
- Best practices and considerations
"""

# Code generation request
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": "Implement JWT authentication middleware for Express.js"}
    ],
    temperature=0.1
)

print(response.choices[0].message.content)

Structured Output (Using Pydantic)

from pydantic import BaseModel
from typing import List

# Define response structure
class CodeSolution(BaseModel):
    explanation: str
    code: str
    test_cases: List[str]
    dependencies: List[str]
    usage_example: str

# Request structured response
completion = client.beta.chat.completions.parse(
    model="gpt-4o",
    messages=[
        {"role": "user", "content": "Write implementation code for Redis connection pool in Node.js"}
    ],
    response_format=CodeSolution,
)

solution = completion.choices[0].message.parsed
print(f"Explanation: {solution.explanation}")
print(f"Code:\n{solution.code}")
print(f"Tests: {solution.test_cases}")

Advanced Data Analysis Usage

# Code Interpreter usage example in ChatGPT Web version
"""
1. Upload CSV file
2. Enter instructions like:

"Analyze this CSV data and:
1. Display basic statistical information
2. Check and handle missing values
3. Create sales trend graphs
4. Generate monthly sales comparison charts
5. Create a comprehensive analysis report

Please also show the Python code."
"""

# Example code executed by ChatGPT (auto-generated)
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# Load data
df = pd.read_csv('sales_data.csv')

# 1. Basic statistics
print("=== Basic Data Statistics ===")
print(df.describe())
print(f"Number of rows: {len(df)}")
print(f"Number of columns: {len(df.columns)}")

# 2. Missing values check
print("\n=== Missing Values Check ===")
missing_data = df.isnull().sum()
print(missing_data[missing_data > 0])

# 3. Sales trend graph
plt.figure(figsize=(12, 6))
df['date'] = pd.to_datetime(df['date'])
daily_sales = df.groupby('date')['sales'].sum()
plt.plot(daily_sales.index, daily_sales.values)
plt.title('Sales Trend')
plt.xlabel('Date')
plt.ylabel('Sales')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

# 4. Monthly sales comparison
df['month'] = df['date'].dt.month
monthly_sales = df.groupby('month')['sales'].sum()
plt.figure(figsize=(10, 6))
plt.bar(monthly_sales.index, monthly_sales.values)
plt.title('Monthly Sales Comparison')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.show()

Function Calling

# External tool integration example
functions = [
    {
        "type": "function",
        "function": {
            "name": "execute_code",
            "description": "Execute Python code and return results",
            "parameters": {
                "type": "object",
                "properties": {
                    "code": {
                        "type": "string",
                        "description": "Python code to execute"
                    },
                    "environment": {
                        "type": "string",
                        "description": "Execution environment (python3, jupyter, etc.)"
                    }
                },
                "required": ["code"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "user", "content": "Write and execute code to generate Fibonacci sequence"}
    ],
    tools=functions,
    tool_choice="auto"
)

# Function calling processing
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    if tool_call.function.name == "execute_code":
        # Actual code execution processing
        code = eval(tool_call.function.arguments)["code"]
        print(f"Code to execute:\n{code}")

Learning Support and Code Review

# Code review feature utilization
review_prompt = """
Review the following code and suggest improvements for:

1. **Performance**: Opportunities for performance enhancement
2. **Security**: Potential security vulnerabilities
3. **Readability**: Code understandability
4. **Maintainability**: Future change adaptability
5. **Best Practices**: Alignment with industry standards

Code:
{user_code}

Please provide specific fix suggestions with explanations.
"""

user_code = """
def get_user_data(user_id):
    import sqlite3
    conn = sqlite3.connect('users.db')
    cursor = conn.cursor()
    query = f"SELECT * FROM users WHERE id = {user_id}"
    result = cursor.execute(query).fetchone()
    conn.close()
    return result
"""

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "user", "content": review_prompt.format(user_code=user_code)}
    ],
    temperature=0.1
)

print(response.choices[0].message.content)