At first, I used the prompt “Help me manage my time”, where Deepseek gave me a super interesting result combining different strategies that I can use:

1. Clarity: Decide What Matters
   Before planning how to spend time, know what you’re optimizing for.

The 80/20 Rule: What 20% of your tasks give you 80% of your results (or fulfillment)? Identify those 1–3 things.

The 3–3–3 Method: Each day, plan:

3 hours on your most important project (deep work).

3 shorter urgent tasks (meetings, emails, admin).

3 maintenance tasks (exercise, chores, learning).

2. Structure: Plan with Intention
   Don’t just react to what’s loudest. Use time blocking.

Time Blocking: Open your calendar. Assign specific blocks of time for deep work, shallow work, and breaks. Treat these blocks as non-negotiable appointments.

Batch Similar Tasks: Do all your calls in one block, all your email in one block. Context switching is a silent time-killer.

The Night Before: Spend 10 minutes each evening planning the next day. It saves hours of morning indecision.

3. Execution: Protect Your Focus
   The best plan fails without execution discipline.

Single-Task: When working, focus on one thing. Close unrelated tabs, put your phone in another room.

Manage Energy, Not Just Time: Are you a morning person? Do your hardest work then. Low-energy after lunch? Schedule admin tasks or a walk.

Use a “Done” List: At the end of the day, list what you did accomplish. This builds momentum and prevents the feeling of “I got nothing done.”

4. Recovery: Avoid Burnout
   Time management isn’t about maximizing every minute. It’s about sustainability.

Schedule Breaks: Use techniques like Pomodoro (25 min work, 5 min break) or simply ensure you take a 5–10 minute break every 90 minutes.

Define Shutdown: Have a clear end to your workday. A ritual (like closing your laptop or changing clothes) signals to your brain that work is done.

These are all useful, however no exact instruction of what “I should really do”, it even told me what matters the most but by not understanding what the most important thing is, because I did not provide other information but jsut to tell me how to manage my time.

Where the other prompt that I used, which is “I am a Junior college student, now I feel like my task is really difficult and I usually sleep around 12. Please help me with my time management when my average out of class time is 4pm.”, worked much better to give actual timetable to follow, tho, still not having other deeper intel because I did not provide any:

Summary of Your New Schedule:
Time Activity
4:00 – 4:15 Transition / Snack / Walk
4:15 – 5:30 HARDEST TASK (Phone away, deep focus)
5:30 – 7:00 Dinner + True Break (No work)
7:00 – 9:00 Easy tasks, review, organization
9:00 – 10:30 Shutdown routine / Wind down
10:30 In bed, lights low, no screens
11:00 Asleep

In this prompt, I used the suggestions from UT Guide:

To get your desired output, it is important to be specific about what that output should look like. When crafting a prompt, remember to try and incorporate these three things:

• Who the author is
• Who the audience is
• What the purpose is
• Example: “please generate a guide written from the perspective of a hiring manager communicating standards of professional attire in a business-casual setting to a group of newly hired student interns”

Which, I did include all these basic components and AI did provide a more specific answer.

The prompt I put in what “Make me a schedule for my day to help me prepare for finals”. The people this prompt and answer I hot would be people who are looking for a good and easy study schedule to follow. The platform I used to get my answer was Claude. Here after plugging in my prompt, it gave me a step by step, hour to hour way to help me study for my exams that I may have. A connection I can make from readings or in class discussions is https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices. This is from checklist March 31st talking about how to use and maintain a good prompt to make it realistic and best as possible. A quote I got from this reading says” Claude responds well to clear, explicit instructions. Being specific about your desired output can help enhance results. If you want “above and beyond” behavior, explicitly request it rather than relying on the model to infer this from vague prompts.” Here it is just explaining to be clear and specific as possible to get the best outcome.

When I asked my prompt this is the answer I received from Claude.

Finals Prep Schedule (Afternoon + Evening)

🕒 3:15–3:30 PM — Quick Reset

📚 3:30–4:30 PM — Study Block #1 (Hardest Subject)

• ☕ 4:30–4:45 PM — Break
• 📘 4:45–5:45 PM — Study Block #2 (Second Hardest Subject)
• 🍽️ 5:45–6:30 PM — Dinner + Mental Reset
• ✏️ 6:30–7:30 PM — Study Block #3 (Easier Subject or Review)
• 🚶 7:30–7:45 PM — Break
• 📑 7:45–8:45 PM — Practice Session
• 🧹 8:45–9:00 PM — Organize for Tomorrow
• 🌙 9:00–10:00 PM — Light Review + Wind Down
• 😴 10:00 PM — Sleep

At the beginning, my starter prompt felt like it could work, but I had a feeling the response would be too broad. I typed in “tell me about World War 1” and the response was a general overview that covered everything at a surface level without really getting into any real meaning or depth. It hit the basics like dates and major battles, but it wasn’t the kind of response that would actually help someone understand the war.

The prompt that really helped was the one where I asked it to be a historian and to focus on the key causes and events of the war. This really changed the answer. The answer was more in depth and really went into the causes of the war. The main causes were militarism, alliances, imperialism, and nationalism. The answer really went into the reasons why these were the causes of the war.

Overall, I learned that AI is good at giving detailed, useful information, but only when you tell it exactly what you want. The first prompt gave me something generic because I gave it nothing to work with. Once I added structure and explicit directions, the quality jumped significantly. As the OpenAI Academy reading states, “prompt engineering is the process of designing and refining your input in a way that helps ChatGPT give the best possible answer” (OpenAI Academy, 2025). In order to get good results, the user has to be intentional with how they frame their prompts, and the more specific the instructions, the better and more focused the output will be.

OpenAI Academy. (2025). Prompting. https://academy.openai.com/public/clubs/work-users-ynjqu/resources/prompting

The LLM prompt I used: Explain how gravity works

The most helpful prompting strategy I’ve learned is using a persona prompt. In class, we have talked about how the way you ask a question changes the model’s answer, and I saw that myself when I asked Copilot to explain gravity. My first prompt just said, “Explain how gravity works,” and the answer was broad. It mentioned Newton and Einstein but did not go into much detail. When I used a persona prompt and asked Copilot to explain it as an astrophysicist, the response became much clearer and more detailed, including examples and explanations that actually made sense. This prompt is helpful for students, writers, or anyone who needs a clear, in depth explanation. By contrast, when I tried an audience prompt explaining gravity in football terms, the answer was less helpful because it simplified things too much.

Using a persona prompt with Copilot showed me how important prompt literacy is, which connects to what I learned from the UT Aspire article. The article talks about how asking clear, thoughtful questions helps students think more deeply and get better answers. I saw this in action when I asked Copilot to explain gravity as an astrophysicist—the answer was detailed and easy to understand. But a broad prompt or one focused on an audience gave weaker results. This shows that knowing how to ask questions carefully can guide thinking and produce the best results, just like UT Aspire explains.

For this experience I choose to try Gemini for the first time, since I’ve always used only ChatGPT and wanted to try something new to see if they differ. My experience was for my surprise really positive, Gemini was asking me questions to be more specific, details about goals in working out, body weight etc. when I tried to ask for workout plan and overall I found this very positive.

For example prompt I used in the 2^nd round

make me a workout plan

gemini answered asking those questions :

 What is your primary goal? (e.g., building muscle, losing weight, increasing endurance, or general health?)

What is your experience level? (e.g., beginner, intermediate, or advanced?)

How many days per week can you realistically commit?

 What equipment do you have access to? (e.g., full gym, dumbbells only, or bodyweight at home?)

After answering those questions it asked me more about body weight and height and after it built me a workout plan acting as a very supportive coach and acting really nice. Workout seemed valid to me and like something that could possibly work if I ever decide to use it, it also gave me options to make a meal plan, which was really adjustable and more options for everyday.

I tried to be as specific as possible and give all of the additional information as I’ve learned from our UT Prompt Literacy guide reading that it’s important to be specific and detailed, iterative refinement – https://www.utsystem.edu/sites/default/files/documents/general-documents/2025/ut-aspire-prompt-literacy/ut-aspire-prompt-literacy.pdf

Overall this was very positive and useful experience for me and I would probably use it again for workout or diets!:)

The most useful prompt guideline that I found was the persona pattern. When I first prompted ChatGPT to “Write me a swim set.” I kept it very bland, and in return, I received a response that was just as bland and simple as what I inputted.

What I received was not what I was looking for and did not match what I had expected. In the following prompt, I assigned Chat the role of being my swim coach and informed it of my history of swimming.

After assigning Chat a role, it was able to write me a much more specific and well-rounded workout to match a Wooster swimmer’s level. Additionally, when prompting Chat the second time around, I gave it specific goals, which seemed to help eliminate the guessing work that Chat was attempting to complete.

Based on what I have learned in this experiment, I can at least confidently say that when asking Chat to create a regimen of some sort, both assigning it a role as well as limits is helpful. Just as stated in the prompting guides, LLMs just predict what you want and offer broad responses, so if you are extremely specific in your ends, you are more likely to meet them (UT Guide). The main thing stated in the UT Guide is that keeping your end goal clear is the most important part of prompting. By assigning Chat a role and setting limits, I was able to match UT’s guide and get an outcome that was much more to my expectations. This can be helpful in the future if I have a specific means and an idea of a product, where I can set limits and expectations while prompting Chat.