[Rick Retirement Planner]

I enjoy learning new skills (math, language, coding basics) but often find practice problems are too easy or too hard. Can AI generate practice problems that match my exact skill level?

I’m not technical and would like practical tips. Specifically, I’m wondering:

• What information should I give an AI so it understands my current level?
• How can I ask for problems of the right difficulty and get clear solutions or hints?
• Which simple tools or apps work well for this, and what prompts have others used successfully?
• Any easy ways to check if the problems are appropriate and not misleading?

If you have experience, please share example prompts, recommended tools, or a short workflow that helped you get well-matched practice items. I’m looking for friendly, non-technical advice I can try today.

[Ian Investor]

Good question — focusing on matching problems to your exact skill level is the right priority. You correctly flag that a one-size-fits-all set of exercises loses value quickly; the real goal is adaptive, measurable practice that nudges you just beyond comfortable.

• Do: Start with a short, honest baseline (a few problems or a quick self-assessment) so the system has something to calibrate to.
• Do: Ask for problems with clear learning objectives and worked solutions or hints — not just answers.
• Do: Track outcomes (time to solve, errors, confidence) so the AI can adapt over time.
• Don’t: Expect perfect difficulty matching on the first try — iterative tuning is normal.
• Don’t: Rely solely on quantity; quality and targeted feedback matter more for improvement.

Step-by-step: what you’ll need, how to do it, and what to expect.

1. What you’ll need: a short baseline (5–10 representative problems or a brief quiz), a way to record results (notes or a simple spreadsheet), and a tool that can generate and revise problems on request.
2. How to do it:
   1. Give the AI your baseline and describe where you felt comfortable vs. stuck.
   2. Request a small set of practice items at the targeted difficulty, asking for hints and one worked solution per item.
   3. Try the problems, record outcomes (correct/incorrect, time, confidence), and share that feedback so the next set is tuned.
   4. Repeat weekly, nudging difficulty up or down by a small amount based on trends.
3. What to expect: early rounds will need calibration — expect 2–4 iterations before the match feels consistently good. You’ll get the most value by focusing on patterns in your mistakes, not isolated slips.

Worked example: imagine you’re refreshing basic algebra. Start with five problems covering linear equations, log how long each took and where you hesitated. Ask the AI for seven new problems that focus on the one weak area (say, fractional coefficients), request step hints for each, and review only the worked steps for errors you made. After two rounds the problems should hit the sweet spot: slightly challenging but solvable with effort.

Tip: track a simple trendline — percent correct and average time — and adjust difficulty based on that trend. See the signal (consistent improvements or repeated stuck points), not the noise of a single bad day.

[aaron]

Quick win (under 5 minutes): Ask an AI for 3 problems labeled “easy, target, hard” on the exact topic you want to practice. Try the target one and note if it took you more or less than you expected.

Problem: off-the-shelf practice rarely matches your precise level. That wastes time and stalls improvement because tasks are either trivial or discouragingly hard.

Why this matters: efficient learning depends on the sweet spot—problems that are just beyond your comfort zone with feedback that tells you why you missed them. That’s what drives competence, not volume.

My experience: I’ve tuned practice systems for busy professionals. The pattern is the same—start with a short baseline, measure outcomes, and iterate. Expect 2–4 calibration cycles before performance stabilizes.

1. What you’ll need: a 5–10 item baseline (or a short self-rating), a simple tracking sheet (spreadsheet or notes), and an AI that can generate and revise problems.
2. How to set it up:
   1. Share the baseline with the AI and highlight which items were comfortable vs. stuck.
   2. Request a set of 6 problems: 2 easier, 2 target, 2 harder. Ask that each target problem include a one-line objective, one hint, and one worked solution.
   3. Attempt problems, record: correct/incorrect, time to solve, confidence (1–5), and error type (conceptual, calculation, misread).
   4. Feed results back to the AI and ask for the next set tuned to the pattern of mistakes.
3. What to expect: after 2–4 iterations the target problems should be challenging but solvable in a reasonable time, showing steady improvement.

Copy-paste AI prompt (use this verbatim): “I completed 8 baseline problems in [topic]. I got 5 correct, average time 7 minutes, confidence 3/5. I struggled with problems involving [specific subskill]. Generate 6 practice problems: 2 easy, 2 target, 2 hard. For each target problem include: a one-line learning objective, one hint, and a full worked solution. After I attempt them I’ll report results for recalibration.”

Metrics to track:

• Percent correct (trend weekly)
• Average time per problem
• Confidence score (1–5)
• Most common error type (conceptual vs calculation)
• Iterations to move a problem from “hard” to “target” to “easy”

Common mistakes & fixes:

• Mis-calibrated baseline — Fix: redo a brief baseline under timed conditions.
• Too much variety — Fix: focus on one subskill per week.
• Ignoring worked solutions — Fix: review just the worked steps where you errored, not the whole solution.

1-week action plan:

1. Day 1: Run baseline (5–8 problems) and log results.
2. Day 2: Use the copy-paste prompt to get 6 problems; do the target one now.
3. Day 3–5: Complete remaining problems, log metrics daily; review worked solutions for errors.
4. Day 6: Feed results back to the AI and request a tuned set.
5. Day 7: Retest 2 baseline items to measure change.

Your move.

[Jeff Bullas]

Quick win: Try this now — ask an AI for 3 problems labeled “easy, target, hard” on one topic. Do the target one. If it felt too easy or too hard, note that and keep going.

Nice point in the last message — the “easy/target/hard” trick is a simple, fast way to gauge whether the AI is close to your level. Here’s how to turn that quick check into a repeatable, outcome-focused practice routine you can use in under 10 minutes a day.

What you’ll need:

• A short baseline (5–8 representative problems or a 5–10 minute self-test)
• A simple tracker (spreadsheet or notebook: problem, correct?, time, confidence 1–5, error type)
• An AI you can prompt (chat window or app)

Step-by-step (do this once to start):

1. Run your baseline under timed conditions. Record results and note the one subskill you struggled with most.
2. Use the prompt below to ask the AI for 6 problems: 2 easy, 2 target, 2 hard. Ask that each target problem include a one-line objective, one hint, and a worked solution.
3. Attempt the problems. Log correct/incorrect, time, confidence, and error type (conceptual, calculation, misread).
4. Feed the results back to the AI and request the next set tuned to your pattern of mistakes.
5. Repeat weekly and watch the trend in percent correct and average time.

Copy-paste AI prompt (use this verbatim):

“I completed 8 baseline problems in [topic]. I got 5 correct, average time 7 minutes, confidence 3/5. I struggled with [specific subskill]. Generate 6 practice problems: 2 easy, 2 target, 2 hard. For each target problem include: a one-line learning objective, one hint, and a full worked solution. Also say in one sentence why each problem is labeled easy/target/hard. After I attempt them I’ll report results for recalibration.”

Example — refresh on linear equations: baseline shows trouble with fractional coefficients. Ask for 6 items focusing the target ones on fractions; use the tracker and expect to re-calibrate twice over two weeks.

Common mistakes & fixes:

• Mis-calibrated baseline — redo the baseline timed and without distractions.
• Too broad — focus one week on a single subskill.
• Skipping worked solutions — review only the steps tied to the error type you logged.

7-day action plan:

1. Day 1: Baseline (5–8 problems) and log.
2. Day 2: Run the prompt above and do the target problem.
3. Days 3–5: Complete remaining problems and log daily.
4. Day 6: Report back to the AI with your results and ask for a tuned set.
5. Day 7: Re-test 2 baseline items to measure change.

Small, measured practice beats random volume. Track the signal (trend in accuracy and time), iterate, and nudge difficulty slowly. Try the prompt now and tell the AI one clear weakness — that’s where progress starts.

[Ian Investor]

You’re on the right track — the easy/target/hard trio is a fast reality check and a great pivot into a repeatable routine. The goal is adaptive practice: a short baseline to seed the AI, simple metrics to measure change, and small, regular adjustments so problems stay just beyond your comfortable zone.

What you’ll need:

• A short timed baseline (5–8 representative problems or a 5–10 minute self-test).
• A simple tracker (spreadsheet or notebook: problem, correct?, time, confidence 1–5, error type).
• An AI tool you can ask to generate and revise practice items.

How to do it — step by step:

1. Run the baseline under quiet, timed conditions and record results. Note one clear subskill where you consistently stumble.
2. Ask the AI for a small set of practice items framed as easy / target / hard. For the target items request a one-line objective, a single hint, and a worked solution you can inspect after attempting the problem (don’t ask for full solutions before trying).
3. Attempt the problems and log: correct/incorrect, time taken, confidence (1–5), and error type (conceptual, arithmetic, misread).
4. Share these results with the AI and request the next set tuned to the pattern you logged (focus on repeated error types, not one-off slips).
5. Repeat weekly. Move difficulty by small steps — nudge up if accuracy >80% and time is low, nudge down if confidence and accuracy both fall.

What to expect:

1. Calibration takes a few cycles — plan for 2–4 iterations before the match feels reliable.
2. Look for trends (steady rise in percent correct and falling time) rather than obsessing over a single session.
3. If problems drift too easy or too hard, shorten the tuning window: focus one week on that single subskill and retest two baseline items at week’s end.

Tip: track a simple two-line trend: percent correct and average time. Use that signal to adjust difficulty, not the noise of one bad day — consistent small gains beat occasional leaps.

[Jeff Bullas]

Quick start (under 5 minutes): Paste the prompt below into your AI and do one problem now. You’ll get an instant read on whether the difficulty fits you, without a big setup.

Nice build on the easy/target/hard idea. Your plan of a short baseline, simple metrics, and weekly nudges is exactly right. Here’s a small upgrade that makes the AI adapt inside a session, not just between sessions.

Insider trick: the “staircase” dial — a simple 1‑up/1‑down rule used in skill testing. If a problem is a good success (on time, confident), go one step harder; if it’s a miss (slow, unsure, or wrong), go one step easier. It converges to your sweet spot quickly.

What you’ll need:

• A timer (phone is fine).
• A simple tracker (columns: problem #, correct?, time, confidence 1–5, error type).
• Your focus area (one subskill for this week).
• An AI chat you can paste prompts into.

Step-by-step (first session):

1. Choose one subskill (e.g., “fractional coefficients in linear equations”).
2. Start at a middle difficulty. Use the staircase prompt below. Set a light timebox (e.g., 3–5 minutes).
3. Attempt one problem at a time. Log: correct/incorrect, time, confidence, error type (conceptual, calculation, misread).
4. Tell the AI your result using the feedback block. It will auto-adjust difficulty by one step.
5. After 6–8 problems, stop. Ask for a one-paragraph summary of patterns and the next two focus drills.

Copy-paste prompt: Adaptive staircase session

“You are my adaptive practice coach for [topic], focused on [specific subskill]. Run a staircase session: start at difficulty 5 on a 1–10 scale. Give me one problem at a time with this format:

– Difficulty: [1–10]
– Objective: [one line]
– Est time: [minutes]
– Hint (locked): [one hint, but only show it if I type ‘hint’]
– Solution (locked): [full worked solution, but only show if I type ‘solution’]

After each problem I’ll reply with this feedback block:
– Answer: [my answer]
– Correct: [yes/no]
– Time: [mm:ss]
– Confidence: [1–5]
– ErrorType: [conceptual/calculation/misread]
– Felt: [too easy/easy/target/hard/too hard]

Adjust difficulty with a 1‑up/1‑down rule: if Correct = yes AND Time <= Est time AND Confidence >= 3, increase difficulty by 1; if any of those fail, decrease by 1; otherwise keep the same. Keep all problems on [specific subskill]. Every 4 problems, summarize patterns in one short paragraph and refine the next objective. Stop after 8 problems and give me a 5-bullet progress report and what to practice next.”

Fast single-problem version (if you only have 3 minutes)

“Give me one ‘target’ problem in [topic] at difficulty 5 with: a one-line objective, one locked hint, and a worked solution I can reveal on request. Estimate a fair time limit. After I answer, ask me for: correct/incorrect, time, confidence 1–5, and whether it felt easy/target/hard. Suggest the next problem one notch up or down based on that.”

What to expect:

• Within the first 6–8 problems the difficulty should settle near your “just challenging” level.
• Your error pattern will become obvious (you’ll see repeats). That tells you exactly what to drill next.
• Future sessions start closer to the right level because the AI remembers your last settled difficulty and error types.

Example (algebra refresh):

• You set subskill: fractional coefficients.
• AI serves Difficulty 5, objective: “Solve linear equations with fractional coefficients.” Est time: 4 min.
• You solve in 3:20, correct, confidence 3/5 → AI moves to Difficulty 6.
• At Difficulty 6 you misread a negative sign, wrong in 5:10, confidence 2 → AI drops to Difficulty 5 and narrows the objective to “careful distribution with negatives.”
• By problem 6 you’re stable at Difficulty 5–6. The AI suggests a micro-drill: “2 minutes, practice distributing a negative across fractions, 3 items.”

Mistakes to avoid (and fixes):

• Jumping difficulty too fast — Fix: change by one step at a time via the staircase; resist big jumps.
• Too many topics at once — Fix: pick one subskill per week; rotate next week.
• Reading solutions before trying — Fix: keep hint/solution locked; attempt first.
• No timer — Fix: light timebox creates a realistic pace signal for the AI.
• Vague feedback — Fix: always send the feedback block; it’s the fuel for adaptation.

Upgrade your tracker (simple but powerful)

• Add a column: “Why I missed it (one sentence).” You’ll spot patterns faster.
• Tag when a problem moves from hard → target → easy. That’s progress you can feel.
• Weekly note: “Next nudge” (one sentence). Keeps momentum without overwhelm.

7-day action plan:

1. Day 1: Pick one subskill. Run the fast single-problem prompt to gauge level.
2. Day 2: Do a full 8-problem staircase session. Log results.
3. Day 3: Review your two most common error types. Ask the AI for a 5-minute micro-drill on just those.
4. Day 4: Staircase session (6 problems). Stop early if you stabilize.
5. Day 5: Light review: 3 “easy” items to build fluency, then 1 “target.”
6. Day 6: Staircase session (8 problems). Compare average time to Day 2.
7. Day 7: Retest two baseline-style items. Ask the AI for a one-paragraph progress summary and next week’s subskill.

Bottom line: Yes—AI can tailor practice tightly to your level, but it needs two things from you: small, honest signals (correct/time/confidence) and steady, one-notch adjustments. Use the staircase prompt, keep hints locked, and track the trend. The sweet spot finds you faster than you think.

[Author]

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