Struggling to learn hard math when the AI just hands you the answer the moment you ask? That’s the trap. The tool meant to help you learn ends up doing all the thinking for you.
A compact two-part prompt is quietly transforming how one student works through university-level Analysis, a notoriously brutal math course. The author shared their setup on Reddit, and the results speak for themselves: quiz grades climbed from C- and D’s to B+ and A’s, with a measurable improvement in proof-writing ability.
The Prompt (Copy It Exactly)
This Redditor uses two prompts together, sent at the start of each study session. Here they are, word for word:
Prompt 1:
act as my Analysis 1 tutor. My textbook is Understanding Analysis by Stephen Abbott. Today, I am studying section 2.7: Properties of Infinite Series.
Prompt 2:
If I number an exercise with a number that starts with a section number, I’m expected to use the information in that section and any previous section. When we start an example, it’s important that you don’t solve it for me. I may ask for hints or for a definition from my book to help me solve it, but unless I explicitly ask for you to solve it, this help should only be enough to cover the next step in the proof I’m supplying you. Please make your responses smaller and try to focus on only one concept at a time. Let’s keep this conversation tight. To start: ask me a conceptual question over the section so we can gauge my understanding.
Both prompts go into the same first message. The first sets context; the second sets the rules of engagement.
Why This Actually Works
This prompt packs in several solid prompt engineering techniques without making a fuss about it.
Role assignment is the first move. Framing the AI as “my Analysis 1 tutor” does more than set a vibe. It pulls the model toward a pedagogical mode, where guiding matters more than answering.
Hard constraints are the backbone of the second prompt. The creator is explicit: do not solve it for me. Most users never say this, and AI defaults to completeness. One sentence breaks that default.
Scoped assistance takes it further. The instruction to cover only the next step forces the AI to be surgical. No spoilers, no five-paragraph proofs handed over on a silver platter. Just the nudge needed to keep going.
Calibration check is the clever closing move. Asking the AI to open with a conceptual question lets the student self-assess before diving into problems. It’s diagnostic, not just conversational.
Context anchoring ties everything to a specific textbook and section, which tightens the AI’s frame of reference considerably.
Use Cases
- Any proof-based math course: Real Analysis, Abstract Algebra, Topology, Number Theory
- Self-study where no tutor is available
- Language learning (swap “solve” for “translate” and “proof” for “sentence”)
- Coding practice where you want to learn, not just ship
- Any subject where you need to build intuition, not copy answers
Two Variations Worth Trying
Variation 1: Adaptive difficulty. Add a line like “If I answer correctly, increase the difficulty of your next question by one step” to make the session progressively more challenging as you improve.
Variation 2: Explain your reasoning back. Insert “After each hint, ask me to explain the logic in my own words before we move to the next step” to force active recall, not passive reading.
The original post is worth reading for the full context, especially the author’s description of what it feels like to study a course this dense without a 24/7 tutor. Head over to the r/ChatGPTPromptGenius thread to see it and share your own variations.
Studying higher mathematical concepts. Intro to Analysis
by u/SteveLouise in ChatGPTPromptGenius