|
Listen to this story
|
Until two years ago, schools and colleges were toiling hard to teach the students C\C++ languages from scratch by printing ‘Hello World’ but now it’s a thing of the past. Following the launch of ChatGPT, English emerged as the new programming language. Lately, a meme has been making the rounds on the internet suggesting that codes generated by ChatGPT take longer for the developers to debug.
On Twitter too, several users expressed disappointment in how difficult it has become to debug the code created by ChatGPT. One of the users on Twitter said, “ChatGPT is good for code generation, but it generates codes that require debugging, so blindly using it would be a waste of time.”
However, is this reason enough to stop them from using ChatGPT for coding? The answer is a big no, because coding and thinking simultaneously puts a break on your chain of thoughts. Even though it takes longer, people would still use ChatGPT for coding because it allows them to be creative, solve problems, and discover new coding ideas. With ChatGPT, our critical thinking ability is not limited by the speed at which we can convert thoughts into codes.
GPT 3.5 vs GPT4
It is a fact that even the most expert human programmer cannot always get the program right on the first try. Large language models (LLMs) have proven to be highly skilled at generating codes, but still face difficulties when it comes to complex programming tasks. To overcome these challenges, researchers have explored a technique called self-repair, where the model can identify and correct errors in its own code. This approach has gained popularity as it helps improve the performance of LLMs in programming scenarios.
A research paper, called ‘Demystifying GPT Self-Repair for Code Generation’, quantifies GPT-4’s self-debug capabilities against other LLMs. According to the paper, GPT-4 has an extremely useful and emerging ability that is stronger than any other model — self-debug.
One of the key findings from the paper was that GPT-3.5 can write much better code given GPT-4’s feedback. GPT-4’s exceptional ability to self-repair stems from its remarkable feedback mechanism. Unlike other models, GPT-4 possesses a unique capacity for effective self-reflection, allowing it to identify and rectify issues within code. This distinguishing feature sets it apart from its counterparts in the AI landscape.
Notably, the feedback model and the code generation model in GPT-4 do not necessarily have to be the same. For example, you can debug the code created by GPT-3.5 using GPT-4. In this case, GPT-3.5 acts as a code generation model and GPT-4 acts as a feedback model. This approach empowers GPT-4 to continuously improve and refine its coding capabilities, making it a standout solution in the field of AI-driven programming.
In an interesting insight from the research, it was seen that GPT-4’s self-generated feedback, along with the feedback provided by an experienced programmer, increased the number of repaired programs. It means human critical-thinking still needs to be a part of the debugging process. AI can assist you with debugging, but in the end, it all boils down to your skills.
What’s next?
The code created by ChatGPT will be as efficient as the prompt. If your prompt is not up to the mark, you will not be able to produce the desired output. Prompting is mostly just trial-and-error, i.e., if one prompt doesn’t work, you try another one. Going ahead, there is a possibility that like coding, you might even not need to create prompts on your own. Developers are coming up with open source models that can be integrated on top of the ChatGPT API that would dish out the best possible prompts for you.
An example of this AI agent is ‘GPT prompt engineer’. It is a constraint agent, which means that its behaviour is highly-controlled, leading to better results than open-ended agents. It chains together lots of GPT-4 and GPT-3.5-Turbo calls that work together to find the best possible prompt. Often, it has even outperformed the prompts written by humans.
ChatGPT, a powerful language model, demonstrates strengths in code conversion, elaboration, and quick prototyping, providing valuable assistance to developers. Its natural language processing capabilities aid in explaining code snippets and fostering collaboration among team members. However, it has limitations, lacking genuine comprehension and context awareness, often producing suboptimal code and errors. Human oversight remains crucial to ensure code alignment with specific project requirements and best practices. Human coders play a vital role in logic formulation, algorithm design, debugging, and troubleshooting. They possess creativity, strategic thinking, and domain expertise that cannot be replicated by AI models. While ChatGPT enhances productivity and efficiency, it should be seen as a complementary tool rather than a replacement for human coders. By leveraging ChatGPT’s strengths and understanding its limitations, developers can achieve enhanced productivity and innovative solutions in software development while retaining the human touch necessary for success.
