Can ChatGPT Crack the Code? Exploring AI’s Role in Solving Coding Challenges

Can ChatGPT Crack the Code? Exploring AI’s Role in Solving Coding Challenges

ChatGPT, the conversational AI that keeps popping up in everything from your toothpaste recommendations to automated customer service, is about to get a new job: coding. We’ve been hearing whispers about AI revolutionizing software development, but how well can it really do under pressure? A recent deep dive into ChatGPT (specifically version 3.5-turbo) has given us some answers, and they’re as exciting as they are enlightening. Spoiler alert: ChatGPT is smart, but even the smartest AI needs some hand-holding outside the box. Let’s break down the research findings and see where ChatGPT shines and where it stumbles when it comes to solving coding problems of varying complexities on LeetCode.

The Coding Challenge Playground: LeetCode

Picture this: you have a coding genie, but this genie is as good as the prompts you give it. This is the scene where ChatGPT struts its stuff on LeetCode, a popular platform buzzing with coding challenges. LeetCode problems range from cuddly teddy bear levels (“easy”) to fire-breathing dragons (“hard”). The study evaluated how well ChatGPT took on these challenges, not just in Python but testing its multilingual capabilities too— from trusty Java to the hidden relics of Elixir and Racket.

ChatGPT vs. Three Titans: The Difficulty Levels

Easy-Peasy Chaos

When it comes to easy problems, ChatGPT strutted like it owned the place, solving an impressive 92% of them. But as the problems went from simple math formulas to more entangled puzzles, its win rate shrank. For medium problems, it still solved a decent 79%, but only managed to conquer 51% of those “hard” horrors. This tells us ChatGPT handles basic tasks fairly well but struggles when things get complicated.

Medium Might

ChatGPT showed its stripes with mid-level challenges where prompt engineering—the art of crafting the perfect question—made a noticeable difference. It’s like telling your friend how you want your burger: specific instructions improve the result.

Hardcore Headaches

The hard problems were a bit more challenging for ChatGPT. Here’s where the magic of user feedback—those annoying little reminders about errors—actually boosted performance. Giving ChatGPT failed test cases to chew on helped it improve, as did switching to GPT-4. This new model came in clutch, reflecting that sometimes newer is indeed better.

Prompt Engineering: The Art of Better Questions

Ever notice how asking your smart device the right question makes all the difference? The same goes for ChatGPT. Introducing concepts like “Chain of Thought” (where the model is guided step by step, like putting together IKEA furniture) really helped, especially for simpler problems. When it knew where to start hanging that shelf, it did so efficiently. However, the fancier the task, the more CeMu-like its understanding had to be.

Feedback Loops

By feeding ChatGPT its failed attempts, we gave it a learning leg up. This was particularly beneficial with medium to difficult problems, where knowing what went wrong allowed it to tweak its approach.

Leveling Up to GPT-4

We also tossed the big brother, GPT-4, into the mix, and guess what? It performed better across the board. Why? Because advanced models don’t need “better” prompts as much—they just get it right, even when the going gets tough.

Speaking Different Tech Tongues

Imagine being asked to write a fairy tale in five languages. Python was where ChatGPT felt most at home—like writing in its mother tongue. It tackled Java and C++ with a fair amount of success too, but languages like Erlang or Racket? Not so much. It’s like trying to narrate that fairy tale using noises only heard in deep forests.

The AI’s ability to work across different coding languages was clearly reliant on its familiarity with them. For languages less represented in its training data like Elixir, ChatGPT hit a wall.

Real-World Implications and Future Applications

Now, what does this all mean for you—whether you’re a full-time coder or just someone who loves dabbling in tech?

1. For Developers: Having an AI that can competently handle simpler, repetitive tasks means more time to innovate. Imagine not having to wrangle with tedious parts of coding and letting ChatGPT do the grunt work.

2. Tech Companies: Slightly refining the AI abilities can result in significant productivity boosts. Also, tapping into more languages can widen market reach and project capabilities.

3. For AI Enthusiasts: Prompt engineering and feedback loops are crucial. Understanding how to guide AI could become an important skill in and of itself.

Key Takeaways

• Difficulty Matters: ChatGPT handles easier problems far better but falters as complexity increases.

• Prompting is Key: Properly structured prompts and utilizing error-focused feedback can significantly improve AI performance.

• Model Evolution: Upgrading to newer AI models like GPT-4 noticeably enhances coding capabilities.

• Language Plays a Role: The AI’s effectiveness varied significantly across programming languages, and it excels where it has ample training data.

• Implications: ChatGPT can revolutionize mundane coding tasks, freeing up developers for more creative work, but there’s still room to grow for complex problem-solving.

In essence, while ChatGPT shows incredible potential, especially for entry-level coding tasks, it’s not replacing the skilled coder anytime soon. With advancements and fine-tuning, its utility in augmenting human capabilities can be far-reaching.

All said, it’s a thrilling time for AI in software development, and as the models get smarter, the future of coding looks promisingly bright! Keep experimenting, keep coding, and keep prompting—who knows, you just might find yourself co-coding with AI soon.

If you are looking to improve your prompting skills and haven’t already, check out our free Advanced Prompt Engineering course.

This blog post is based on the research article “Evaluating ChatGPT-3.5 Efficiency in Solving Coding Problems of Different Complexity Levels: An Empirical Analysis” by Authors: Minda Li, Bhaskar Krishnamachari. You can find the original article here.