You're making decisions based on guesses. And those guesses are...
Shushant Lakhyani@shushant_l
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Nov 30, 2025
~3 min read
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You're making decisions based on guesses.
And those guesses are costing you accuracy, time, and clarity.
Here are 10 new ChatGPT prompts that finally fix your probability-based reasoning (open this) 👇
And those guesses are costing you accuracy, time, and clarity.
Here are 10 new ChatGPT prompts that finally fix your probability-based reasoning (open this) 👇
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1. Conditional Probability Breakdown
Prompt: Using the following scenario, explain the conditional probabilities step by step and walk me through how to solve it like a tutor. Break the explanation into assumptions, formulas, substitutions, and final values. Scenario: [Insert scenario]. Clearly identify P(A), P(B), and P(A|B), check for independence, and show how the answer changes if one variable is altered by [Insert change].
Prompt: Using the following scenario, explain the conditional probabilities step by step and walk me through how to solve it like a tutor. Break the explanation into assumptions, formulas, substitutions, and final values. Scenario: [Insert scenario]. Clearly identify P(A), P(B), and P(A|B), check for independence, and show how the answer changes if one variable is altered by [Insert change].
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2. Bayes’ Theorem Reasoning
Prompt: Apply Bayes’ theorem to this problem and return the answer with complete reasoning. Define prior, likelihood, evidence, and posterior explicitly. Scenario to analyze: [Insert scenario]. Use symbolic representation first, then numeric substitution. Also provide an interpretation of what the posterior probability means in simple language and how modifying [Insert variable] changes the result.
Prompt: Apply Bayes’ theorem to this problem and return the answer with complete reasoning. Define prior, likelihood, evidence, and posterior explicitly. Scenario to analyze: [Insert scenario]. Use symbolic representation first, then numeric substitution. Also provide an interpretation of what the posterior probability means in simple language and how modifying [Insert variable] changes the result.
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3. Multi Event Probability Tree
Prompt: Create a full probability tree diagram in text format for the scenario: [Insert scenario]. Label all branches, show joint probabilities, marginal probabilities, and conditional probabilities. After building the tree, compute the probability of [Insert event], explain how each branch contributes, and highlight any sources of dependence or independence.
Prompt: Create a full probability tree diagram in text format for the scenario: [Insert scenario]. Label all branches, show joint probabilities, marginal probabilities, and conditional probabilities. After building the tree, compute the probability of [Insert event], explain how each branch contributes, and highlight any sources of dependence or independence.
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4. Expected Value Analysis
Prompt: Calculate the expected value of this probabilistic decision making scenario: [Insert scenario]. Break the output into outcomes, probabilities, payoff values, calculations, and interpretation. Then provide a sensitivity analysis where you adjust one of the probabilities by [Insert variable] and recompute the EV to show how decision making could change.
Prompt: Calculate the expected value of this probabilistic decision making scenario: [Insert scenario]. Break the output into outcomes, probabilities, payoff values, calculations, and interpretation. Then provide a sensitivity analysis where you adjust one of the probabilities by [Insert variable] and recompute the EV to show how decision making could change.
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5. Monte Carlo Simulation
Prompt: Run a conceptual Monte Carlo simulation with at least 1,000 iterations (no code needed) for this event: [Insert scenario]. Explain the model setup, assumptions, random variable behavior, number of trials, and estimated probability distribution. Then provide a summarized result, mean, variance, and interpretation of uncertainty.
Prompt: Run a conceptual Monte Carlo simulation with at least 1,000 iterations (no code needed) for this event: [Insert scenario]. Explain the model setup, assumptions, random variable behavior, number of trials, and estimated probability distribution. Then provide a summarized result, mean, variance, and interpretation of uncertainty.
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6. Probabilistic Decision Making Under Uncertainty
Prompt: Analyze this decision problem using probability weighted outcomes: [Insert decision]. Create a comparison table of options, list the probabilities and payoff values, compute expected utility, and recommend the optimal choice based on rational probabilistic reasoning. Highlight any cognitive biases a decision maker might have and how probability clarifies the decision.
Prompt: Analyze this decision problem using probability weighted outcomes: [Insert decision]. Create a comparison table of options, list the probabilities and payoff values, compute expected utility, and recommend the optimal choice based on rational probabilistic reasoning. Highlight any cognitive biases a decision maker might have and how probability clarifies the decision.
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7. Probability Distribution Identification
Prompt: Based on the scenario below, determine the most appropriate probability distribution (binomial, geometric, Poisson, normal, exponential, etc.) and justify why. Scenario: [Insert scenario]. After identifying the correct model, compute the probability of [Insert event], show every step, and explain how the distribution parameters influence the result.
Prompt: Based on the scenario below, determine the most appropriate probability distribution (binomial, geometric, Poisson, normal, exponential, etc.) and justify why. Scenario: [Insert scenario]. After identifying the correct model, compute the probability of [Insert event], show every step, and explain how the distribution parameters influence the result.
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8. Random Variables and Independence
Given the scenario: [Insert scenario], identify all random variables involved and determine whether they are independent or dependent. Show how the probability calculation changes in each case. Then compute the joint probability P(A and B), marginal probabilities, and conditional probabilities P(A|B) and P(B|A) with complete stepwise reasoning.
Given the scenario: [Insert scenario], identify all random variables involved and determine whether they are independent or dependent. Show how the probability calculation changes in each case. Then compute the joint probability P(A and B), marginal probabilities, and conditional probabilities P(A|B) and P(B|A) with complete stepwise reasoning.
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9. Risk Assessment with Probability
Prompt: Perform a complete probability-based risk assessment on this situation: [Insert scenario]. Define all risk events, assign probability ranges, assign impact values, compute expected risk value (probability multiplied by impact), and rank the risks from highest to lowest. Then propose mitigation strategies based on the probability-weighted impact.
Prompt: Perform a complete probability-based risk assessment on this situation: [Insert scenario]. Define all risk events, assign probability ranges, assign impact values, compute expected risk value (probability multiplied by impact), and rank the risks from highest to lowest. Then propose mitigation strategies based on the probability-weighted impact.
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10. Real World Probability Estimation
Prompt: Estimate the likelihood of [Insert real world event] using a structured reasoning approach. Break the analysis into reference class forecasting, base rate selection, adjustment factors, likelihood range, and final numeric estimate. Provide best-case, worst-case, and most realistic probability values, and explain the assumptions in each case.
Prompt: Estimate the likelihood of [Insert real world event] using a structured reasoning approach. Break the analysis into reference class forecasting, base rate selection, adjustment factors, likelihood range, and final numeric estimate. Provide best-case, worst-case, and most realistic probability values, and explain the assumptions in each case.
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