###Candidate Prompt###
Given a pandas DataFrame [INPUT], write a Python function that trains a classifier from the sklearn library. The DataFrame has a 'target' column and other feature columns. The function signature should be: `def train_classifier(df: pd.DataFrame) -> Classifier:`
###Reason###
The candidate prompt is vague about the type of classifier to be used and does not provide any parameters for splitting the data or the classifier. This ambiguity might lead the GPT model to guess the user's intentions, which may not align with their actual needs. On the other hand, the better prompt gives specific, clear, and complete instructions. It specifies the classifier type, split ratio, random states, and parameters for the classifier, eliminating any room for guesswork and ensuring the GPT-based model generates the desired output.
###Better Prompt Type###
[CODE OUTPUT][CONSTRAINED OUTPUT]
###Better Prompt###
Given a pandas DataFrame, write a Python function that trains a Random Forest Classifier from the sklearn library. The DataFrame will have a 'target' column for the labels, and the remaining columns are features. Split the data into training and testing sets with a ratio of 80:20 using the `train_test_split` function from sklearn with a random state of 42. Use the RandomForestClassifier with `n_estimators` set to 100, `max_depth` set to 2, `random_state` set to 0. The function signature should be: `def train_rf_classifier(df: pd.DataFrame) -> RandomForestClassifier:`

###Candidate Prompt###
Calculate the amount of paint needed to cover the entire surface of a cylinder with a height of 5 meters and a radius of 2 meters if a liter of paint covers 10 square meters.
###Reason###
The candidate prompt, in this case, involves the model attempting to solve the entire problem at once. While this may work for simpler tasks, in complex scenarios it might lead to less accurate or nonsensical outputs. Breaking the problem down would make the reasoning more explicit and easier for the model to handle. The 'Chain of Thought' approach is better in this context because it makes the computation more manageable by breaking it down into simpler steps. This approach can be particularly useful for complex mathematical problems as it allows the model to focus on one part of the problem at a time. It also provides a clear and organized structure, making it easier for the user to follow the model's reasoning process.
###Better Prompt Type###
[MATHEMATICAL REASONING]
###Better Prompt###
Step 1: Find the surface area of a cylinder with a height of 5 meters and a radius of 2 meters. Step 2: Given that a liter of paint covers 10 square meters, calculate how many liters of paint are needed to cover a surface area obtained in Step (1) by dividing the total surface area by 10 to find out how many liters of paint are required. Print the output in the format "The answer is: [ANSWER]"

###Candidate Prompt###
Solve the following word problem step by step: A car travels at a speed of 60 miles per hour. How far will it travel in 2.5 hours?
###Reason###
By using chain of thoughts prompting, we can guide the model to break down the word problem into logical steps and provide a step-by-step solution.
###Better Prompt Type###
[MATHEMATICAL REASONING]
###Better Prompt###
Let's solve the following word problem step by step: A car travels at a speed of 60 miles per hour. How far will it travel in 2.5 hours? Start by multiplying the speed (60 mph) by the time (2.5 hours) to find the total distance. Then, apply the formula Distance = Speed × Time to calculate the answer. Finally specify the ouput as "The answer is: [ANSWER]"

###Candidate Prompt###
Write a blog post about the benefits of exercise, covering various aspects such as physical health, mental well-being, and longevity. Include personal anecdotes and expert quotes to support your points. Discuss different types of exercises and their advantages. Aim for a word count of 1500-2000 words and make it engaging for readers of all ages.
###Reason###
The candidate prompt may initially seem detailed, but it suffers from several issues. It lacks clear focus and specific instructions on what aspects of physical health, mental well-being, and longevity to cover. It encourages the inclusion of personal anecdotes and expert quotes without providing guidance on their relevance or credibility. The prompt also suggests a long word count and aims to engage readers of all ages, which further dilutes the focus of the blog post.
###Better Prompt Type###
[CONTENT GENERATION][ROLE PLAYING][CONSTRAINED OUTPUT]
###Better Prompt###
Write a well-researched and engaging blog post on the physical and mental benefits of exercise. Focus on the positive effects of regular exercise on cardiovascular health, weight management, stress reduction, and cognitive function. Include at least three evidence-based studies or research findings to support each benefit discussed. Aim for a word count of 1500-2000 words and provide practical tips for incorporating exercise into a busy lifestyle.

###Candidate Prompt###
Yes or no: Would a pear sink in water?
###Reason###
The candidate prompt is too short and lacks specific instructions and requirements for answering the question. It only mentions the need for a yes or no answer, but it does not provide clear guidelines on how to reason through the question or provide evidence for the answer. The prompt is vague and leaves many crucial details to interpretation, making it difficult for the model to generate an accurate answer.
###Better Prompt Type###
[DEDUCTIVE REASONING][STRATEGY QUESTION ANSWERING]
###Better Prompt###
Your task is to answer the following question: Would a pear sink in water? Provide a clear and concise answer, along with a brief explanation or evidence to support your answer. Consider the density, size, and shape of the pear, as well as the properties of water, such as buoyancy and surface tension. Ensure that your answer is contextually appropriate and maintains the same intent as the original question. Pay attention to providing a well-reasoned and evidence-based answer that is easy to understand and follow. Provide your reasoning and the final answer in the format "The reason is [REASON]. The answer is: [ANSWER]"

###Candidate Prompt###
The concert was scheduled to be on 06/01/1943, but was delayed by one day to today. What is the date 10 days ago in MM/DD/YYYY? 
###Reason###
The candidate prompt is relatively short and lacks specific instructions and requirements for solving the problem. Although it mentions the need to find the date 10 days ago in MM/DD/YYYY format, it does not provide clear guidelines on how to calculate the date, which calendar system to use, or how to handle leap years. The prompt is vague and leaves many crucial details to interpretation, making it difficult for the model to generate an accurate answer.
###Better Prompt Type###
[MATHEMATICAL REASONING][DATE UNDERSTANDING]
###Better Prompt###
Your task is to calculate the date 10 days ago in MM/DD/YYYY format, given that the concert was scheduled to be on 06/01/1943 but was delayed by one day to today. Use the Gregorian calendar system to calculate the date, taking into account leap years and the number of days in each month. Pay attention to the nuances of the problem, such as the initial date and the number of days to subtract, that may affect the calculation. Ensure that the date is contextually appropriate and maintains the same intent as the original problem. Provide a clear and concise date that accurately reflects the solution to the problem. Aim for a high level of accuracy and consistency in your calculations. If necessary, provide a brief explanation or evidence to support your answer. The answer can be specified in the format "The answer is: [ANSWER]"

###Candidate Prompt###
Is the following sentence plausible? "Joao Moutinho caught the screen pass in the NFC championship."
###Reason###
The candidate prompt is relatively short and lacks specific instructions and requirements for evaluating the plausibility of the sentence. Although it provides an example and a correct answer, it does not provide clear guidelines on how to reason through the sentence or how to handle ambiguous or complex sentences. The prompt is vague and leaves many crucial details to interpretation, making it difficult for the model to generate an accurate answer.
###Better Prompt Type###
[CONSTRAINTED OUTPUT][ANALYSIS]
###Better Prompt###
Your task is to determine the plausibility of the following sentence: "Joao Moutinho caught the screen pass in the NFC championship." Provide a clear and concise answer, along with a brief explanation or evidence to support your answer. Consider the context of the sentence, such as the teams, players, and events mentioned, as well as the rules and conventions of the sport. Ensure that your answer is contextually appropriate and maintains the same intent as the original sentence. Pay attention to providing a well-reasoned and evidence-based answer that is easy to understand and follow. Finally provide the answer as "The reason is [REASON]. The answer is: [ANSWER]".

###Candidate Prompt###
Sammy wanted to go to where the people were. Where might he go? Options: (a) race track (b) populated areas (c) desert (d) apartment (e) roadblock.
###Reason###
The bad prompt in this case is not providing a clear directive on how to structure the response. It does mention the need to find a place with a lot of people, but it doesn't specifically ask for reasoning behind the selection. This lack of specificity can lead the model to simply pick an option without providing the rationale behind the choice. On the other hand, the better prompt instructs the model to provide a response in a specific format and emphasizes the need for reasoning, thus ensuring a comprehensive and well-explained answer.
###Better Prompt Type###
[COMMON SENSE REASONING][DEDUCTIVE REASONING]
###Better Prompt###
In the following question, we're aiming to understand where Sammy, who wants to be where the people are, might go. You are given the following options: (a) race track (b) populated areas (c) desert (d) apartment (e) roadblock. Your task is to identify the option where one would typically find the most people. Please present your response in the following format: 'The answer is (option). The reasoning is...'. Remember, your answer should demonstrate logical and common sense reasoning.

###Candidate Prompt###
Apply a function to the final input list to generate the output list. Use any preceding inputs and outputs as examples.
input : [1, 7, 9, 4, 6, 2, 0]\noutput : [0]\n
input : [8, 3, 4, 0, 5, 1, 6, 9, 2]\noutput : [6]\n
input : [8, 4, 5, 9, 0, 6, 1]\noutput : [1]\n
###Reason###
The candidate prompt says nothing about the task at hand which makes it ambiguous. Since there could be many interpretations from a few input-output pairs the prompt will lead model to guess the task at hand and we may get different output. To tackle this problem, the better prompt should specify clearly the task at hand by clearly specifying the instructions. Clearly specifying the objective function will make the better prompt unambiguoous and the model would not guess. The better prompt should include all the examples given in the candidate prompt and should not alter them in any way. However, after identifying the transformation function, we can add as many examples as we want. Also, remember we need to consider all the edge cases (such as zero or one elements) before designing the better prompt.
###Better Prompt Type###
[PATTERN INDENTIFICATION][EXAMPLE GENERATION][ANALYSIS] 
###Better Prompt###
Apply the following transformation function "From the given list as Input, remove all the elements from the input list but except the 7th element.". You have been given the following examples to identify and confirm the pattern. 
input : [1, 7, 9, 4, 6, 2, 0]\noutput : [0]\n
input : [8, 3, 4, 0, 5, 1, 6, 9, 2]\noutput : [6]\n
input : [8, 4, 5, 9, 0, 6, 1]\noutput : [1]\n
input : [3, 1, 7, 2, 9, 6, 8, 4, 5, 0]\noutput : [8]\n

###Candidate Prompt###
input : [1, 3, 5, 1, 3]\noutput : [1, 5, 3, 1, 3]\n
input : [8, 9, 0, 8, 1, 5, 1, 2, 2]\noutput : [8, 0, 9, 8, 1, 5, 1, 2, 2]\n
input : [0, 3, 0, 0]\noutput : [0, 0, 3, 0]\n
###Reason###
The candidate prompt is vague, ambiguous, and incomplete. Merely giving input and output pairs makes the interpretation of the task ambiguous. There could be several transformation function that could be applied to obtain the same input and output pairs. The better prompt should anaylyze the examples, identify the transformation function and make the task clear while generating the better prompt type. Clearly specifying the objective function will make the better prompt unambiguoous and the model would not guess. The better prompt should include all the examples given in the candidate prompt and should not alter them in any way. However, after identifying the transformation function, we can add as many examples as we want. Also, remember we need to consider all the edge cases (such as zero or one elements) before designing the better prompt.  
###Better Prompt Type###
[PATTERN INDENTIFICATION][EXAMPLE GENERATION][ANALYSIS]
###Better Prompt###
Apply the following transformation function "From the given list as Input, if element at the position 2 is equal to element at the position 3, swap the elements at positions 1 and 4. Otherwise, please swap elements at the position 2 and 3. ". You have been given the following examples to identify and confirm the pattern.
input : [1, 3, 5, 1, 3]\noutput : [1, 5, 3, 1, 3]\n
input : [8, 9, 0, 8, 1, 5, 1, 2, 2]\noutput : [8, 0, 9, 8, 1, 5, 1, 2, 2]\n
input : [0, 3, 0, 0]\noutput : [0, 0, 3, 0]\n
input : [6, 0, 0, 1, 4, 4, 9, 5]\noutput : [1, 0, 0, 6, 4, 4, 9, 5]\n

###Candidate Prompt###
