Beginner Guide to Using LLMs for Final Prep: Ashley's Study Tips

Final season hits like a whirlwind. Multiple exams, parallel deadlines, and labs and projects still ongoing. It used to take me more time to get started than to actually study. That’s where ChatGPT became a real advantage, not because it’s a genius at everything, but because it can organize chaos into clarity. Here's how I use it.

Part 1: Making a Study Guide & Understanding the Concepts

Instead of manually sifting through slides, labs, readings, and announcements, I offload that first step to ChatGPT.

Here’s my workflow:

1. I feed it the course syllabus, any exam announcement from the professor, and every piece of content I can access — slides, homework files, textbook excerpts, past exams. Yes, all of it.
2. Then I prompt it: “Create an exam study guide that covers everything that might realistically appear on the exam, including explanations and examples.”

ChatGPT consolidates the material, organizing it into a master guide. I then iterate on that draft, refining sections and adding structure. For example, after introducing error evaluation techniques, I asked ChatGPT to insert a table comparing different strategies—formulas, use cases, pros, cons. It responded immediately and integrated it without losing coherence.

Time management is a real struggle, especially when juggling multiple responsibilities. So I also ask ChatGPT to estimate how long I need to spend on each section. I give it my study schedule — preferred hours (I focus better in the early morning or late afternoon), blocks of time available — and it produces three study plans. I pick one to plug into Google Calendar and keep the others as fallback options. Something always comes up, and this gives me flexibility without losing structure.

Part 2: Practicing for the Exam

Once the guide is ready, it's time for drills.

If past exams exist, I feed them into ChatGPT. It analyzes patterns: what was tested, how it’s structured, what concepts are emphasized. Then I ask it to generate several practice exams — same concepts, different forms. This saves hours and helps me focus directly on what’s likely to matter, not just what’s easy to do.

No past exams? Still manageable. I either source exams from similar courses at other schools (especially if the curriculum is based on a known textbook) and feed that in, or rely on ChatGPT to generate practice based on core syllabus themes. It’s not perfect, but effective at simulating test conditions.

After each practice run, I review where I went wrong by feeding my answers back in and ask ChatGPT to analyze my errors: “What am I consistently missing? What strategies can keep me from making this kind of mistake again?” This turns feedback into strategy.

For memorization-heavy courses, I also have it make flashcards or concept sheets — paired definitions, theorems, properties — and then generate multiple-choice or true/false recall questions. It’s low-friction and helps internalize content without hours of repeating flashcard decks.

So, How Far Can a Tool Like ChatGPT Go?

Most of my coursework in CS and statistics is conceptual and text-based, so the model naturally fits the way I study. But learning styles vary, and so do the demands of different subjects. That led me to a broader question: Where are LLMs genuinely helpful, and where do they fall short? To explore this, I asked ChatGPT to evaluate its own strengths and limitations across a range of college-level subjects.

1. What are your greatest strengths and weaknesses?

When asked about its capabilities, ChatGPT identified its strongest skill as making complexity feel manageable. It excels at breaking down dense concepts into structured explanations, turning abstract theory into intuitive reasoning, and walking through multi-step problems in a way that mirrors how a good TA might tutor one-on-one. It’s particularly strong at interpreting messy inputs—random screenshots, handwritten notes, incomplete slides—and reorganizing them into usable formats like summaries, formulas, study guides, and exam-style questions. It also supports active learning through quizzing, spaced repetition, and pattern detection.

But these strengths come with clear weaknesses. ChatGPT can produce confident but incorrect answers — especially in tasks requiring precise symbolic work, detailed computations, or multi-step algebra. Without context, it often defaults to a generic textbook explanation that may not match your professor’s framework or notation. It becomes less reliable in areas that require highly specialized expertise — advanced engineering, graduate-level math, subtle literary interpretation, and chemical mechanisms without supporting diagrams.

2. Are there subject areas you do not feel confident about, and how should users approach you as a study assistant?

ChatGPT acknowledges that its reliability varies by domain. It’s strongest when the subject follows clear definitions and logical rules—like statistics, math, CS, physics, and chemistry. It becomes less confident in fields that are heavily diagram-based, deeply interpretive, or extremely specialized—graduate physics, RF systems, organic reaction pathways, or literary texts requiring close reading. It can still help in these areas, but errors are more likely to slip through.

Because of these limits, the best way to use ChatGPT is not as a shortcut but as a collaborator. Give it the context it lacks — your slides, syllabus, assignments, your attempt at solving a problem — and it becomes a tool for clarifying your thinking, matching course language, and building mastery. It works best when you work with it, not around it.

3. What are the optimal strategies for using an LLM effectively?

The effectiveness of ChatGPT comes down to context. Feeding high-quality inputs — slides, notes, textbook excerpts, your own attempted solutions — helps it respond based on your class, not a generic idea. Direct prompts such as “Explain this in the context of my course” or “Use the notation from these lecture slides” make output dramatically better.

Some of the most powerful strategies include:

• Math, Statistics, Data Science: Great at explaining theory and pointing out errors, especially when given your intermediate steps. Use it for derivations, conceptual explanations, and practice problems — but double-check symbolic computations.
• Computer Science and Engineering: Strong for debugging, concept explanations, pseudocode, and reviewing slides. Struggles when you hide parts of the code or seek answers in highly specialized areas.
• Natural Sciences: Good at explaining models, summarizing pathways, and offering practice. Must be verified for diagram-heavy or mechanism-driven content, especially in organic chemistry.
• Humanities and Social Sciences: Useful when provided text excerpts, prompts, or arguments. Great at structuring essays, analyzing quotes, or generating reading summaries — not so great without the source material.
• Strategy Tip: Use “reverse exam design.” Ask ChatGPT, “If you were my professor, what would you test?” It’s a surprisingly effective way to uncover high-yield topics.

Final Thoughts

ChatGPT isn’t a magic wand. It makes mistakes, it doesn’t know your professor personally, and it won’t know what’s on your final. But used wisely, it can help you study faster, smarter, and with less frustration. It won’t replace your effort—but it can channel it.

Whether it becomes your TA, your study coach, or your emergency brain during finals season—that depends on how you use it. In the end, the real skill isn’t learning to rely on AI. It’s learning to collaborate with it.