Steal My 2-Prompt Blueprint: Turn ChatGPT Into Your Personal AI Tutor (Live Demo)

A two-prompt “blueprint” turns ChatGPT into a personal AI tutor by treating prompting as a learning system—not a one-off request. The core move is to structure prompts around a repeatable workflow (diagnose → teach → practice → escalate) and to use “switches” (hard vs. easy, agentic vs. lighter modes, effort levels, output formats) so the model adapts to a learner’s level and pace. That matters because small wording and sequencing changes can dramatically reshape what the model does next—whether it quizzes methodically and gatekeeps until answers are complete, or it delivers micro-lessons immediately with single-question diagnostics.

The “hard mode” prompt is built to generate a custom learning tutor prompt for the user. It starts by assigning a role (“prompt coach”) and a shared mission: craft a prompt blueprint that quizzes methodically to diagnose the learner’s current level and then delivers progressively harder lessons. The prompt’s architecture follows a four-part framework—Purpose, Instructions, Reference, Output—and it insists on workflow discipline: go section by section, don’t skip ahead, ask the full question set, gatekeep until all answers are sufficiently clear, and carry confirmed answers forward using memory so the user isn’t repeatedly re-asked. It also forces the user to specify learning “stance” and constraints (e.g., interrogative tone, allowed references, output length in words or tokens, and formatting like Markdown or JSON). To deepen the model’s reasoning, it includes reference examples as placeholders that signal the desired depth across different domains (e.g., pricing strategy, content calendar, agentic monitoring), without pasting full prompts that could hijack the model into the wrong task.

The “easy mode” prompt targets learners who don’t want to complete a long setup. It keeps the same overall mission—diagnose current level and deliver progressively harder lessons—but adds constraints designed for quick consumption: single-question mode, micro-lessons, and a cap of no more than five questions at a time. Each micro-lesson follows a tight loop: ask a diagnostic question, teach a concept, give a practice task or code snippet, and optionally add a harder challenge only after the learner scores above 80% on the prior practice. It also introduces pacing and control features (batch up to three questions, shorten lessons, ask for progress summaries) and emphasizes active learning tactics like mini-projects, code snippets, thought experiments, and authoritative sources.

In a live run, hard mode first produces a table of many setup questions and example answers, then assembles the final “learning tutor” prompt once the user completes the required inputs. Easy mode, by contrast, begins teaching immediately: it responds to a learner’s answer, expands it with structured explanations (including flashcard-style framing), then continues with the next diagnostic question—illustrated with backpropagation and validation/test data.

The takeaway is practical: both prompts achieve the same tutoring goal, but flipping early “switches” (question cadence, micro-lesson structure, gatekeeping, and default effort) changes the learning experience. Hard mode effectively becomes a meta-prompt generator—prompting that builds a custom prompt—while easy mode fills in defaults so learners can start right away. The result is a reusable approach for crafting AI tutors tailored to different knowledge levels and time constraints.