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AI feedback tool

Improve AI feedback effectiveness for every poker decision.

Enter the hand context, your chosen line, and the assumptions behind it. The coach adapts across price calls, thin value, split-pot traps, live-card reads, 3-bet pots, blind defense, multiway boards, short-stack pressure, and emotional overplays.

Price Value Pressure Habits

Decision review

AI poker strategy coach.

Use this when you want a focused answer to: how can I get feedback that changes by poker scenario, names the missing assumptions, and turns the next rep into a clear action?

AI poker strategy coach

AI feedback effectiveness for your poker decision.

Use the AI Poker Feedback Tool by entering the spot, the action you chose, and your reasoning. The coach scores the thought process, checks the details you provide, and gives a focused correction loop.

  1. 1. FramePick the game, street, action, and review depth so the coach knows the decision type.
  2. 2. EvidenceAdd hand texture, opponent action, pot price, and the learning target you want tested.
  3. 3. ReviewSubmit your reasoning, then use the output cards to keep, fix, and drill one habit.

Ready to review a poker decision.

Tailored scenario suggestion

Current template fits the entered hand.

As you add hand details, the coach recommends the best scenario template for the feedback.

  • Primary suggestion will update from your game, action, hand text, opponent notes, and learning goal.

Frame the decision

Required context
Main question
Review depth

Add the facts the coach can test

Better input, better feedback
Decision context
Hand details for better feedback

Add concrete facts the coach can test. Example: Ah Kh on Qh 8h 3c 2s, villain bet turn after check-calling flop.

Explain your thought process

Guided prompt

Price and equity

I chose this action because the pot was offering __ to __, and I estimated my clean equity from __.

Range and opponent

I think villain's range is __, worse hands that continue are __, and better hands that fold are __.

Next street plan

On safe cards I will __, on scary cards I will __, and on blanks I will __.

Personal trigger

The emotion or habit that affected this line was __, and the smaller disciplined option was __.

Before generating feedback, check for these details
  • The exact decision you made and the alternative you considered.
  • The pot price or bet size that made the spot close.
  • One opponent-range claim and one card-texture assumption.
  • Any stack-pressure, multiway, or emotional factor that changed the normal answer.

Coach output

Good price awareness, but the plan needs cleaner outs.

  • ScoreHow complete the decision process is.
  • KeepThe habit worth repeating.
  • FixThe assumption to correct next time.
  • DrillThe shortest practice rep to run.
Decision score76Study quality
ClarityStrongReasoning depth
RiskMediumLeak pressure
Next repPot oddsPractice focus
Math disciplineReadyPrice and equity
Range evidenceNeeds workOpponent logic
Plan depthPartialNext streets
CalibrationAlignedConfidence fit
Scenario template

Drawing hand price check

Start with the break-even call price, discount dirty outs, and compare calling against the semi-bluff line.

Common trap: counting every improving card as clean when the opponent can already dominate part of the draw.

Primary checkBreak-even equity versus clean outs.
Opponent filterWhich better hands continue or fold.
Next repReplay the hand after removing one optimistic out.

Outcome visualization

Best caseClean outs are live.
62%
Base caseCurrent evidence quality.
48%
Leak riskUnproven assumptions punish the line.
34%

Better price and cleaner outs lift the base case; missing range evidence increases leak risk.

Personalized feedback

Calling is reasonable because the price is good, but the reasoning is incomplete until you count clean outs and explain why raising is not better.

Personalized AI tips

These tips adapt to your hand details, learning goal, opponent profile, and weakest decision metric.

  • Math firstWrite the break-even price, then discount dirty overcard outs before defending the call.
  • Opponent filterCompare calling with raising by naming better hands that fold and worse hands that continue.
  • Next repReplay the spot after removing one optimistic out and lowering confidence until the evidence improves.
  • Learning goalUse this hand to stop overvaluing dirty overcard outs in similar turn spots.

Adaptive learning plan

Generate or edit the spot to create a next-session plan from your current leak, saved review history, and beta confidence signal.

Priority leakMath disciplineStart with break-even price and clean outs.
Replay drillPrice ladderChange the bet size and rerun the same decision.
Table cueAsk before actingWhat assumption must be true for this line?
Confidence targetRate after reviewTrack before and after confidence to prove the feedback helped.

Historical performance analysis

Historical performance analysis starts after this first saved review.

Saved reviews0New baseline
Average score76/100Saved decision quality
Current vs averageFirst baselineScore trend context
Recurring leakNone yetWeakest metric
Style patternBalancedRecent profile
Consistency rangeNew rangeScore spread over saved reviews
Next adjustmentStart reviewBased on prior decisions
Tester confidenceNot ratedDecision confidence lift
Confidence check Evidence matches confidence

Your confidence is usable because the reasoning mentions price and a missing out-count.

Range evidence One missing range claim

Add the exact better hands you expect to fold or the worse hands you expect to continue.

Replay plan Run the price first

Replay the hand by changing the call cost, then remove one optimistic out and compare the score.

Learning target Tie confidence to price and equity.

Write the break-even point, discount dirty outs, and lower confidence when the math depends on optimistic assumptions.

Decision audit

Pot priceCall needs about 17% equity.
Pressure pointMedium price, verify clean outs.
Opponent filterBalanced range requires evidence.
Position noteMiddle position limits information.
Learning targetMath confidence
Playing styleBalanced style keeps the advice evidence-first.
Math disciplinePrice mentioned; equity needs a cleaner out count.
Range evidenceName better folds and worse continues.
Future planAdd safe, scary, and blank runouts.
Calibration gapConfidence is close to the process grade.

Missing assumptions

  • Count clean outs before defending the call.
  • Name which better hands can fold or worse hands can continue.
Keep

Price first

You recognized that fixed-limit pot size can justify continuing without forcing a raise.

Fix

Clean outs

Separate cards that make your hand from cards that make a second-best hand.

Drill

One-sentence plan

Before acting, say what you will do on safe, scary, and blank runouts.

Next study actions

  • Write the break-even price before defending the action.
  • Mark which outs are blocked, dirty, or only win half the pot.
  • Compare call and raise by naming who folds and who calls worse.

Line comparison

Calling keeps the price manageable, but raising needs a clear fold-equity or value target.

Decision tests

  • If the price gets worse, the call needs cleaner outs or more implied value.
  • If the opponent is tighter, downgrade bluff-catching and thin value.

Before you submit the spot

  • Include your exact hand, visible cards, and the opponent action that created the decision.
  • Say what feedback would change your next session.

Copyable review prompt

Prompt ready for a deeper AI or study-group review.

Engagement signal

Each generated review sends a lightweight analytics event when site analytics are enabled, which can be used to track adoption of the coach.

Dynamic guide

Start with the decision score, then compare math discipline, range evidence, plan depth, and confidence calibration before choosing the next drill.

More tools

Game selection toolChoose a variant that matches the skill you want to improve. Rotation simulatorTurn review notes into mixed-game reset practice.

AI guidance

This coach is a study aid for hand review. Recheck pot size, action order, range assumptions, and game-specific rules before using any recommendation at the table.

Rate this feedback

Mark whether the response was accurate and useful so the study loop can be evaluated.

Accuracy
Helpfulness
Before confidence
After confidence
Confidence liftNot ratedRate before and after confidence to tune future feedback.
Tester patternNew signalNo beta confidence ratings saved yet.
Adaptive responseStart ratingThe coach will adapt tips when confidence does not improve.
No rating submitted yet.