While ChatGPT boasts countless features, there is one that appeals to programmers the most: the ability to generate code.
ChatGPT has proven to be capable of generating functional code in a matter of seconds. As a result, it has transformed how programmers tackle their coding projects, with many saving time and energy by using the chatbot to handle the tedious or cumbersome aspects.
However, ChatGPT has a track record of generating ineffective code. In fact, StackOverflow previously imposed a temporary ban on content created by ChatGPT and other generated AI, citing their low rate of correct answers as the primary reason.
That being said, such outcomes often have less to do with the quality of the underlying large language model (LLM) and more to do with how the users interact with the chatbot.
People will reap more benefits from their chatbots once they learn to harness prompt engineering, a subject that has been gaining a lot of attention as of late.
Here, we give a brief overview of why well-written prompts are so important for ChatGPT and how data scientists can utilize prompt engineering to get the most value out of this chatbot.
Why Prompts Matter
Despite the generative capabilities of ChatGPT, the chatbot has a record of generating bad code. In fact, StackOverflow had initially imposed a temporary ban on ChatGPT due to an influx of incorrect answers that it derived.
Code generated from ChatGPT is shunned for various reasons, with some being suboptimal and others being irrelevant to the problem altogether. However, regardless of the issue, the underlying cause is the same: inadequate prompts.
Models like GPT-3.5 and GPT-4.0 are very powerful, but they are sensitive to user-provided prompts. ChatGPT, powered by GPT-3.5, can only operate successfully when given prompts that provide sufficient context with appropriate language.