Research into the art of asking the right question(s).
After some back and forth with ChatGPT in trying to produce better prompts, especially for inquiries into various topics that I’m curious about, I realized it mainly boils down to better framing questions, no different than research projects. Prompts and meta-prompts are what we input into LLMs to execute a task. We can ask open-ended questions to an LLM by inserting a regular prompt like one would normally use Google for, such as, ‘What happened during WW2?’ or ‘How did WW2 begin?’ You could ask the AI to reframe that question to better ones.
I believe that we also need to sharpen and hone our skills and ask better questions for better outputs from the LLM, like ChatGPT. People often complain about LLMs not answering questions correctly, hallucinating, etc. But it often depends on the user’s input (us) tinkering and interacting with the AI to actually produce a great response.
It’s not just a matter of “prompt engineering” one needs to get a great handle on when using AI, but also in engineering questions for the best results. Would you rather have garbage in, garbage out, or gold in, and diamonds out? I prefer the latter, metaphorically speaking. You don’t have to be a master prompt engineer; you need to master all sorts of different questions, from broad to precise questions. Additionally, one must understand what a question actually is in and of itself, and what makes a question distinguishable from other forms or types of questions.
This inquiry was to understand questions in and of themselves, and when it is appropriate to apply them. As someone once said, “Philosophy is the art or science of asking questions.” Once you have the correct framed question(s), you are halfway towards the answer. As a researcher, it is incredibly useful to know about the intricacies of questions. Having a manual or an atlas of questions for effective inquiry is a game-changer when delving into the different rabbit holes I find myself getting in. Question Engineering is the mental model and a meta-hack to save yourself from lots of headaches when researching or just asking your LLM something.
NotebookLM:
The provided text introduces Question Engineering, a rigorous framework that treats inquiry as a formal discipline rather than an intuitive skill. It establishes the Question Atlas to categorize queries by their functional intent, truth conditions, and cognitive depth across domains like science, law, and philosophy.
To improve information retrieval, the sources detail a Sequence Playbook containing specific algorithms for resolving conflicts, uncovering deception, and maximizing mathematical certainty.
The framework also includes a Question Debugger designed to identify and repair “pathological” inquiries that suffer from logical flaws, hidden assumptions, or linguistic ambiguity.
By utilizing specialized scoring rubrics, this system evaluates the quality and efficiency of questions to ensure they act as precise tools for discovery.
Ultimately, the methodology bridges the gap between traditional human epistemology and the modern requirements of Large Language Model prompt engineering.
Social Scholarly 