Instead of just asking a broad question, I learned that framing my prompt based on who I am makes the response way more helpful. Whether I was stuck at my internship or needed skincare tips for my oily, acne-prone skin, telling ChatGPT exactly what I needed (like being an 11th grader or a beauty enthusiast) helped me get answers that actually made sense. This method is super useful for anyone who wants personalized responses.
After reading a paper in class regarding different ways to prompt AI, it allowed for more creative outlook on how to use AI (White et al., 2023). The most used type of prompting if I recall was in the form of persona. This is where you ask AI to impersonate whatever you would like. This is where creativity comes into it. I had Gemini (2.0 Flash) pretend it was an interviewer to try and see what kind of generic questions would come my way when I get that far into the job search. The specific prompt was, “You are a interviewer and I am the person you are interested in hiring, what questions would you ask me that would most relate to the geology field?” trying to focus on the field that I would be applying to. I understand most jobs will ask more specific questions regarding to what the company does, but I felt this is a good starting place for generic thought provoking questions. This kind of prompting goes further than just interviews. This simulates any kind of important conversation someone might have in which they want to prepare for. The less likely you are to be caught off guard, the better.
The response was not exactly what I had expected in terms of a conversation between interviewer and interviewee, but there were really good points brought up in why Gemini asked specific questions. It started with a generic greeting, and went straight into a line of questioning. What I found really neat about these though are the fact Gemini would explain what type of questions they were and what purpose that question would achieve by answering.
Link to paper cited: https://app.perusall.com/courses/writing-with-ai-sp25-gmds-19907-01/white-et-al-prompt-pattern-catalogue?assignmentId=6H83biAZq4wnKiRbg&part=1
Today I discovered that Perplexity even has trouble understanding the more complex prompts. But that was for only one style of prompting. Other than that it worked really well. For me personally, since being introduced to it, Perplexity is the most efficient LLM I have used. One big flaw I have realized is how bad Perplexity is to following directions. I have had it follow directions for about 4 prompts until it resorts to a different method, I would then tell it to go back to my directions. So, for me that is my failure of the blog post. I still have high hopes for it as the LLM develops through time. Since being introduced, Perplexity has been more efficient in following directions, though still lacking behind other models.
For me Perplexity has been the most effective for helping me with my course work. Mainly for helping me build a guide for papers or schedules. I started using Claude to assist me write code for my CSci project. Then I started only using ChatGPT for day-to-day questions and finding articles for sports and fitness
A helpful prompting tip that I’ve learned from my own experience is to read the information you provide carefully and ask the AI clear questions. One time, while using ChatGPT to ask for a review of a movie I had seen as the director of the movie, the AI gave information about the characters in the movie, but the storyline was not the same as the movie I’ve watched. Then, when I rephrased the question without giving clear information for it like “Are you sure about that detail?”, it immediately responded, “Sorry for that misinformation, let me fix it for you”. After revision, the information given was correct. However, I wanted to test how AI received the information, so I asked again, “I thought this scene should be like this, no?”. It confirmed that the initial answer given was correct and ignored the correct information answered later. Since it’s been a while, I can’t find it in the chat history with AI.
I have learned my lesson every time I use ChatGPT to ask questions. You want to make sure that you specify your question and look through your materials clearly to prevent unnecessary errors. As we have read in the reading, “An LLM can always produce factual inaccuracies, just like a human” (White et al., 2023). So don’t rely too much on AI to answer everything for you.
My group compared Google Gemini, xAI (Grok), and Deep Seek.
Our prompt: “you’re a kitchen designer, I want to remodel my kitchen which is 4 meters long, 3 meters wide, and 2.5 meters tall. On the south side there is a big arch which covers the whole wall. Right next to it on the east side there is a doorway to the outside and exactly opposite there is a door to a staircase. On the east side there are two windows. I want you to ask me questions about the design of the kitchen one-by-one and if you have enough information I want you to describe and then generate an image based on my answers”
This is an example of both the Persona Pattern (Page 6 of the White et al article) and Chain of Thought Prompting which we talked about in class.
This prompt would be most helpful to DIY enthusiasts and homeowners while planning renovations. AI-powered image generations allows this audience to visualize their desired outcome by experimenting with different designs without committing to expensive changes. This prompt can also serve as a starting point for consultations with professional designers.
I used a chatbot called Grok, developed by xAI. I chose this LLM because I had never worked with it before, or even heard of it until it was mentioned in class today.
xAI’s response:
Great! I’m excited to help you design your kitchen remodel. With the dimensions you’ve provided (4m long, 3m wide, 2.5m tall) and the layout details, I’ve got a basic picture of the space. Now, let’s refine the design with some questions to ensure it fits your needs and style:
Once you answer these, I can describe the design and, if you’d like, confirm whether you want me to generate an image of it. What do you think?
We found that the first responses amongst all three LLM’s were really similar! The first question each LLM asked us in response was about the functionality of the kitchen, and how we would use it.
Unfortunately, I realized after class that xAI is run by Elon Musk, which led me to immediately deleting my account. Please don’t forget the importance of carefully researching the corporations and individuals who benefit from our use, to avoid making the same mistake I did!!
n today’s class, I used the chatgpt4o model to conduct an LLM class experiment. I broke the routine and used non-academic topics about sports. I asked chatgpt to be my golf coach, and I asked the chatgpt coach how to improve my technical performance on the golf course (focusing on my irons). The answer given by this “coach” was to first improve my hitting point, understand the landing distance of each iron, etc. Maybe gpt thought I was a golf novice, but in fact I am a high-level professional player. When I told the “coach” my handicap (a measure of the level of a golfer), gpt gave me a strategy for approaching the hole more aggressively instead of just asking to hit the green. In addition to technology, the “coach” shared with me how to show my level under pressure.
LLM has unique advantages in the use of AI. AI has a very powerful database in different disciplines, and has the language processing ability to organize this data (knowledge) into easy-to-understand language for users. If users can organize specific sentences for AI or other language models, these models can give very suitable feedback.
（Partial interaction record with chatgpt）
