How Your Questions Shape AI Output
When you type out a question or a task for ChatGPT, you’re doing what is known as ‘prompting’. Think of it as starting a conversation. How you phrase your question, the details you include, and even the context – they all guide the AI in crafting its response or what is known as the output.
ChatGPT is able to understand your prompts based on a vast amount of data that it has been trained on. GPT 3.5’s knowledge is up to date as of 2021. So, when you ask something, it’s using all that knowledge to figure out what you mean and how best to answer.
The Right Way to Ask
Using clear and detailed questions in your prompts is the best way to get clear and detailed outputs – basically you need to put in what you hope to get out. If you are too vague, the AI might not give you a clear and concise answer and may generate hallucinations1 rather than facts.
Keeping track of previous prompts
One of the cool things about ChatGPT is its ability to remember what was said earlier in the interaction. Once you have opened a chat and begin prompting, the outputs may require new or edited prompts to further give additional information. If you need to refer back to a prompt or output in the thread GPT can remember what was asked, suggested or used. This means you can have a back-and-forth exchange that feels more natural and will help achieve the desired output without having to start from the very beginning.
Learning from Examples
ChatGPT can also learn from examples. If you show it how you expect an answer to be by giving examples, it can tailor its responses better. It’s a bit like teaching someone by showing them how it’s done.
The way you interact with ChatGPT is about the questions you ask, and the tasks you set in your prompts. How you phrase your prompts shapes the conversational output. It’s a blend of your input and the AI’s intricate understanding of language, all coming together to create a chat that’s informative, engaging, and sometimes even surprising!
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- Outputs that are nonsensical or altogether inaccurate. https://www.techtarget.com/whatis/definition/AI-hallucination ↩︎
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