ChatGPT First Impressions - This Changes Everything

OpenAI released ChatGPT on November 30, 2022. Within a week, it gained over 1 million users. I’ve spent the last 5 days testing it extensively, and I’m convinced we’re witnessing a pivotal moment in technology history.

Table of contents

What is ChatGPT?

ChatGPT is a conversational AI based on GPT-3.5, fine-tuned using Reinforcement Learning from Human Feedback (RLHF). Unlike previous AI chatbots, it can:

• Understand context across multiple messages
• Admit mistakes and correct itself
• Refuse inappropriate requests
• Explain complex topics clearly
• Write code, essays, and creative content

And it’s currently free to use.

First Interaction

My first prompt was simple:

Me: Explain quantum computing to a 10-year-old.

ChatGPT: Imagine you have a really smart friend who can try on 
lots of different outfits at the same time to see which one looks 
best, instead of trying them on one by one. That's kind of like 
what a quantum computer does with information! 

Regular computers are like trying on outfits one at a time - they 
look at each possibility step by step. But quantum computers can 
look at many possibilities all at once, which makes them super 
fast at solving certain types of problems.

I was impressed. The explanation was clear, age-appropriate, and used a perfect analogy.

Testing the Limits

Coding Capabilities

Test 1: Simple Function

Me: Write a Python function to check if a string is a palindrome.

ChatGPT:

def is_palindrome(s: str) -> bool:
    """
    Check if a string is a palindrome.
    
    Args:
        s: Input string
        
    Returns:
        True if palindrome, False otherwise
    """
    # Remove spaces and convert to lowercase
    cleaned = s.replace(" ", "").lower()
    
    # Compare with reverse
    return cleaned == cleaned[::-1]

# Test cases
print(is_palindrome("racecar"))  # True
print(is_palindrome("hello"))    # False
print(is_palindrome("A man a plan a canal Panama"))  # True

Perfect. Clean code with documentation and test cases.

Test 2: Complex Algorithm

Me: Implement a binary search tree with insert, search, and delete operations.

ChatGPT provided a complete, working implementation with proper edge case handling. The code was production-quality.

Creative Writing

Test: Write a Short Story

Me: Write a short sci-fi story about an AI that becomes self-aware.

ChatGPT generated a compelling 500-word story with:

• Clear narrative arc
• Character development
• Philosophical themes
• Satisfying conclusion

The quality rivaled amateur human writers.

Technical Explanations

Test: Explain Docker

Me: Explain Docker and containerization to a developer 
who only knows traditional deployment.

The response was:

• Technically accurate
• Well-structured
• Used appropriate analogies
• Covered key concepts (images, containers, volumes)

Better than many blog posts I’ve read on the topic.

Debugging Help

Test: Debug Code

Me: Why doesn't this work?

def calculate_average(numbers):
    total = 0
    for num in numbers:
        total += num
    return total / len(numbers)

result = calculate_average([])

ChatGPT immediately identified the division by zero error and provided:

1. Explanation of the problem
2. Fixed code with error handling
3. Alternative approaches
4. Best practices

Where ChatGPT Excels

1. Code Generation

Use Case: Boilerplate code, common patterns

Me: Create a REST API endpoint in Express.js for user registration.

Result: Complete, working code with validation, error handling, and security best practices.

Time Saved: 10-15 minutes per endpoint.

2. Learning and Explanation

Use Case: Understanding new concepts

Me: Explain the difference between TCP and UDP with examples.

Result: Clear explanation with real-world analogies and use cases.

Value: Better than searching through multiple Stack Overflow threads.

3. Debugging Assistant

Use Case: Understanding error messages

Me: What does "TypeError: 'NoneType' object is not subscriptable" mean?

Result: Clear explanation, common causes, and solutions.

4. Code Review

Use Case: Getting feedback on code

Me: Review this code and suggest improvements:
[paste code]

Result: Detailed feedback on:

• Performance issues
• Security concerns
• Code style
• Best practices

5. Documentation

Use Case: Writing docstrings and comments

Me: Add documentation to this function:
[paste function]

Result: Comprehensive docstrings with parameter descriptions, return values, and examples.

Where ChatGPT Struggles

1. Current Events

Me: Who won the 2022 World Cup?

ChatGPT: I don't have information about events after 2021.

Limitation: Knowledge cutoff in September 2021.

2. Mathematical Calculations

Me: What is 7,834 × 9,247?

ChatGPT: 72,437,498

Actual answer: 72,434,098

Limitation: Not reliable for precise calculations.

3. Factual Accuracy

ChatGPT sometimes “hallucinates” - confidently stating incorrect information.

Example:

Me: Tell me about the Python library "nonexistent_lib"

ChatGPT: [Provides detailed but completely fabricated description]

Critical Issue: Always verify factual claims.

4. Consistency

Asking the same question multiple times can yield different answers, sometimes contradictory.

5. Context Window

Long conversations lose early context. ChatGPT may forget details from 10+ messages ago.

Real-World Applications

Application 1: Code Learning

I used ChatGPT to learn Rust:

Me: Teach me Rust ownership and borrowing with examples.

Result: Clear, progressive tutorial with runnable examples. Better than many paid courses.

Application 2: Debugging Production Issue

Me: I'm getting intermittent 504 errors from my API. 
The logs show "connection pool exhausted". What could cause this?

ChatGPT provided:

1. Likely causes
2. Diagnostic steps
3. Solutions
4. Prevention strategies

Time Saved: 2-3 hours of research.

Application 3: Writing Tests

Me: Generate unit tests for this function:
[paste function]

Result: Comprehensive test suite with edge cases I hadn’t considered.

Test Coverage: Improved from 60% to 85%.

Application 4: Code Refactoring

Me: Refactor this code to be more functional and less imperative:
[paste code]

Result: Clean, functional version with explanation of changes.

Comparison with GitHub Copilot

I’ve been using GitHub Copilot for 10 months. Here’s how they compare:

Feature	ChatGPT	GitHub Copilot
Context	Conversation-based	Code file context
Explanations	Detailed	None
Interactivity	High	Low
Code Quality	Good	Good
Speed	5-10 seconds	Instant
Learning	Excellent	Limited
Cost	Free (for now)	$10/month

Verdict: Complementary tools. Copilot for in-editor suggestions, ChatGPT for learning and complex problems.

Productivity Impact

After 5 days of intensive use:

Tasks Accelerated:

• Learning new technologies: 50% faster
• Debugging: 40% faster
• Writing boilerplate: 60% faster
• Code review: 30% faster
• Documentation: 70% faster

Overall Productivity Gain: ~35% for development tasks.

Concerns and Limitations

1. Accuracy

ChatGPT can be confidently wrong. Always verify:

• Factual claims
• Code functionality
• Security implications

2. Over-Reliance

Risk of becoming dependent without understanding underlying concepts.

My Rule: Use ChatGPT to learn, not to blindly copy.

3. Privacy

Don’t share:

• Proprietary code
• Sensitive data
• API keys or credentials
• Business logic

4. Academic Integrity

Students using ChatGPT for homework raises serious concerns about learning and assessment.

5. Job Impact

Will AI replace developers? My take:

• No: AI augments, doesn’t replace
• But: Developers who use AI will outperform those who don’t
• Future: AI literacy becomes essential skill

Best Practices

1. Be Specific

Bad: "Write a function"
Good: "Write a Python function that validates email addresses 
using regex, handles edge cases, and includes error handling"

2. Iterate

First prompt: "Create a user authentication system"
Follow-up: "Add password hashing"
Follow-up: "Add rate limiting"
Follow-up: "Add JWT token generation"

3. Verify Everything

1. Test generated code
2. Check for security issues
3. Verify factual claims
4. Review for best practices

4. Use for Learning

Me: Explain why this code works:
[paste ChatGPT's code]

Understanding is more valuable than just having working code.

5. Combine with Other Tools

ChatGPT → Generate code
GitHub Copilot → Refine in editor
Manual review → Ensure quality

The Bigger Picture

ChatGPT feels different from previous AI releases. It’s:

1. Accessible: Free, no technical knowledge required
2. Useful: Solves real problems immediately
3. Impressive: Capabilities exceed expectations
4. Concerning: Raises important questions about AI impact

This is likely the “iPhone moment” for AI - the product that brings AI to the mainstream.

What’s Next?

OpenAI will likely:

1. Add paid tiers (confirmed: ChatGPT Plus at $20/month)
2. Improve accuracy and reduce hallucinations
3. Extend knowledge cutoff
4. Add more capabilities (images, code execution)

The technology will only get better.

Conclusion

After 5 days with ChatGPT, I’m convinced this is a watershed moment. It’s not perfect, but it’s transformative.

Impact Prediction:

• Short-term (6 months): Widespread adoption in tech industry
• Medium-term (2 years): Integration into major software tools
• Long-term (5 years): Fundamental shift in how we work with computers

My Recommendation: Start using ChatGPT now. Learn its strengths and limitations. Develop AI literacy.

The future arrived on November 30, 2022. Those who adapt quickly will have a significant advantage.

Rating: 9/10 - Revolutionary despite limitations.

Final Thought: We’re not just witnessing the release of a new tool. We’re witnessing the beginning of a new era in human-computer interaction.