Large-language model (LLM) systems like ChatGPT are all the rage lately and everyone is racing to figure out how to use them. People are screaming that LLMs are going to put them out of jobs, just like the Luddite movement thought so many years ago.
A big problem is that a lot of people do not understand what things like ChatGPT are and how to use them effectively. Things like ChatGPT rely on statistics. They are trained on huge amounts of text and learn patterns from that text. When you ask them a question, they parse through it and then see what patterns they learned that statistically appear to be the most relevant to your input and then generate output. ChatGPT is a tool that can be effective at helping you to get things done, as long as you keep a few things in mind while using it.
You should already know something about your question before you ask.
Nothing is perfect, and neither are large-language models. You should know something about the problem domain so that you can properly interpret the output you get. LLMs can suffer from what is termed hallucination, where they will blissfully answer your question with incorrect and made-up information. Again, their output is based on statistics, and they’re trained on information that has some inherent biases. They do not understand what you are asking like another human would. You need to check the answer to determine if it is correct.
If you are a software developer, this is especially true when asking ChatGPT to write code for you. There are plenty of examples online of people going back and forth with it until they get working code. My own experience is that it has major issues with the Python bindings for GDAL for some reason.
Be clear with what you ask
ChatGPT uses natural language parsing and deep learning to process your request and then try to generate a response that is statistically relevant. Understand that getting good information out of a LLM can be a back and forth, so the clearer you are, the better it can process what you are asking. Do not ask something like “How do I get rich?” and expect working advice.
Be prepared to break down a complex question into smaller parts
You will not have much luck if you ask something like “Tell me how to replace the headers in my engine” and get complete and specific advise. A LLM does not understand the concept of how to do something like this, so it will not be able to give you a complete step-by-step list (unless some automobile company tries to make a specific LLM). Break down complex questions into smaller parts so that you can combine all the information you get at the end.
Tell it when it is wrong
This is probably mainly important for software developers, but do not be afraid to tell ChatGPT when it is wrong. For example, if you ask it to write some source code for you, and it does not work, go back and tell it what went wrong and what the error was. ChatGPT is conversational, so you may have to have a back and forth with it until it gives you information that is correct.
Ask it for clarification
The conversational nature of ChatGPT means that if you do not understand the response, you can ask it to rephrase things or provide more information. This can be helpful if you ask it about a topic you do not understand. Asking for clarification can also help you to judge whether you are getting correct information.
NEVER GIVE IT PERSONAL INFORMATION
Do NOT, under any circumstances, give ChatGPT personal information such as your social security number, your date of birth, credit card numbers, or any other such information. Interactions with LLMs like ChatGPT are used for further training and for tweaking the information it presents. Understand that anything you ask ChatGPT will permanently become part of its training set, so in theory someone can ask it for your personal information and get it if you provide it.
Takeaways
ChatGPT is a very useful tool, and more and more LLMs are being released on an almost weekly basis. Like any tool, you need to understand it before you use it. Keep in mind that it does not understand what you are asking like a human does. It is using a vast pool of training data, learned patterns, and statistics to generate responses that it thinks you want. Always double check what you get out of it instead of blindingly accepting it.