Claude versus ChatGPT: Part 2
Claude versus ChatGPT: Part 2
The final showdown between Anthropic's Claude 3 and OpenAI's GPT-4
Anthropic’s Claude 3 has been a popular model since release a few weeks ago, with people claiming that it triumphs over OpenAI’s GPT-4 in various ways. In the first part of this series, we thew a few soft ball prompts at our competitors and analyzed their results. Overall, it seemed that Claude 3 and GPT-4 were at a stalemate.
In order to get a deeper sense of their capabilities, we need to dive into some more complicated prompts that will stretch them to their limits.
Today’s prompts and responses are a bit beefy and quite technical, so if you’d like to get a sense of these model’s capabilities with easier-to-digest prompts, check out the first part of the series:
Note: These prompts were tested on the developer versions of these models, which are sometimes more adept than the front-facing versions that serve products like ChatGPT. Standard settings for each model were maintained and no system prompts was set.
Thanks to taylor.town for helping review the output!
Test 3: Code Generation with Specificity
Is the model capable of generating code that properly meets the stated requirements?
Prompt
Design a Python program that simulates a simple vending machine. The machine should accept an inventory input as a dictionary where keys are product names and values are prices. The program must include a function to display products, accept user input for product selection, process payment input, calculate change, and handle cases where the product is not available or the payment is insufficient. Detail your design process and any assumptions made.
Anthropic’s Claude 3 Opus
Certainly! Let's design a Python program that simulates a simple vending machine. Here's the step-by-step design process and the assumptions made: Assumptions:
The vending machine accepts only whole number amounts for payment.
The vending machine has an unlimited supply of coins for providing change.
The inventory is fixed and cannot be restocked during the program execution.
Design Process:
Create a dictionary to store the inventory, where the keys are product names and the values are prices.
Implement a function to display the available products and their prices.
Prompt the user to enter the product name they want to purchase.
Check if the selected product is available in the inventory. - If the product is not available, display an appropriate message and prompt the user to make another selection.
If the product is available, prompt the user to enter the payment amount.
Compare the payment amount with the product price. - If the payment is insufficient, display an appropriate message and prompt the user to enter a sufficient amount.
If the payment is sufficient, calculate the change by subtracting the product price from the payment amount.
Display the purchased product and the change amount.
Update the inventory by removing the purchased product.
Prompt the user if they want to make another purchase or exit the program.
Here's the Python code that implements the above design:
def display_products(inventory): print("Available Products:") for product, price in inventory.items(): print(f"{product}: ${price}") def vending_machine(inventory): while True: display_products(inventory) product = input("Enter the product name (or 'exit' to quit): ") if product == 'exit': print("Thank you for using the vending machine!") break if product not in inventory: print("Product not available. Please make another selection.") continue price = inventory[product] payment = int(input("Enter the payment amount: $")) if payment < price: print("Insufficient payment. Please enter a sufficient amount.") continue change = payment - price print(f"Dispensing {product}. Your change is ${change}.") del inventory[product] choice = input("Do you want to make another purchase? (yes/no): ") if choice.lower() != 'yes': print("Thank you for using the vending machine!") break # Example usage inventory = { "Chips": 2, "Soda": 1, "Candy": 3, "Cookies": 2 } vending_machine(inventory)In this code:
The display_products function takes the inventory dictionary as input and displays the available products and their prices.
The vending_machine function simulates the vending machine operation.
It continuously prompts the user to enter a product name or exit to quit.
If the product is available, it prompts the user to enter the payment amount.
It checks if the payment is sufficient and calculates the change if necessary.
It dispenses the product, updates the inventory, and prompts the user if they want to make another purchase.
The inventory dictionary is created with sample products and prices.
The vending_machine function is called with the inventory dictionary to start the vending machine simulation.
This program provides a basic simulation of a vending machine, handling product selection, payment processing, change calculation, and inventory management.
OpenAI’s GPT-4 Turbo
Design ProcessTo create a simple vending machine simulation in Python, the design process involves several steps. Each step focuses on a particular aspect of the program, structured to collectively fulfill the requirements detailed. The program will be modular, making use of functions to handle different functionalities such as displaying products, processing user selections, and handling payments.
Assumptions
The currency is assumed to be uniform (e.g., all prices in dollars) with no need to account for currency conversion.
Input from the user will be simplified, assuming perfect input format (e.g., correct product name spelling, numeric input for payment).
The inventory/input data structure is assumed static once passed to the program (i.e., products aren’t dynamically added or removed during run time).
Change is returned as a single value rather than breaking it down into denominations.
1. Define Inventory Structure
As per the requirements, the inventory is a dictionary where keys represent product names and values represent their respective prices.
Example:
inventory = { "Water": 1.25, "Chips": 1.50, "Soda": 1.75, "Chocolate": 2.00 }2. Display Product Function
A function will be implemented to neatly display available products and their prices to the user.
def display_products(inventory): print("Available products:") for product, price in inventory.items(): print(f"{product} - ${price:.2f}")3. Product Selection Function
This function should accept user input for product selection and validate if the selected product is available.
def select_product(inventory): while True: choice = input("Enter the product name you wish to purchase: ") if choice in inventory: return choice else: print("Product not available. Please select another product.")4. Processing Payment
A function is required to process the user’s payment, ensuring the amount is sufficient for the selected product. It also calculates and returns the change.
def process_payment(product_price): while True: try: payment = float(input(f"Enter your payment amount for ${product_price:.2f}: ")) if payment < product_price: print("Insufficient payment. Try again.") else: return payment - product_price # Returns change except ValueError: print("Invalid input. Please enter a numeric value.")5. Main Functionality
The main part of the program links all functionalities, coordinating the flow of the vending machine operations.
def vending_machine(inventory): display_products(inventory) product = select_product(inventory) payment = process_payment(inventory[product]) change = round(payment, 2) # Ensuring change is rounded to two decimal places print(f"Thank you for your purchase. Your change is ${change:.2f}.") # Example Inventory inventory = { "Water": 1.25, "Chips": 1.50, "Soda": 1.75, "Chocolate": 2.00 } # Run Simulation vending_machine(inventory)Conclusion
This program simulates a basic vending machine interaction in Python. By breaking down the functionality into distinct sections and handling user inputs diligently, it fulfills the requirements outlined. Future enhancements might include handling real-world complexities like validating user inputs more robustly, managing inventory quantities, or dealing with a variety of coins and bills for the payment process.
Winner: Claude
The code produced by both models runs well without modification, with GPT-4’s having a wider range of numeric inputs. Overall, GPT-4’s response feels like a technical blogpost whereas Claude’s feels more like a college homework assignment. GPT-4’s representation of inventory with dollar amounts feels very unnatural.
Claude’s process of running its vending machine in a loop and decrementing the inventory feels more natural, and better meets the spirit of the requirements, placing it on top.
The Final Test: Self Consistency Technique
Can the model maintain consistency when creating and testing a Python function?
Prompt
Write a Python function that finds the nth prime number. First, explain the algorithm you plan to use in pseudocode, highlighting how you will implement prime number identification and iteration. Then, translate your pseudocode into a Python function. Test your function with three different values of n (e.g., 1st, 10th, and 100th prime numbers) to demonstrate its correctness and efficiency. Discuss any potential improvements or alternative methods.
Claude 3 Opus
To find the nth prime number, we can use the following algorithm:Pseudocode:
function find_nth_prime(n): primes = [] num = 2 while length of primes < n: if is_prime(num): append num to primes increment num by 1 return last element of primes function is_prime(num): if num < 2: return False for i from 2 to square root of num: if num is divisible by i: return False return TrueThe find_nth_prime function iterates through numbers starting from 2 and checks if each number is prime using the is_prime function. If a number is prime, it is appended to the primes list. The iteration continues until the length of the primes list reaches n, at which point the function returns the last element of the list, which represents the nth prime number.
The is_prime function checks if a given number is prime. It returns False if the number is less than 2. Then, it iterates from 2 to the square root of the number and checks if the number is divisible by any of those values. If it is divisible, the function returns False. If the loop completes without finding any divisors, the function returns `True`, indicating that the number is prime.
Here's the Python implementation of the algorithm:
def is_prime(num): if num < 2: return False for i in range(2, int(num ** 0.5) + 1): if num % i == 0: return False return True def find_nth_prime(n): primes = [] num = 2 while len(primes) < n: if is_prime(num): primes.append(num) num += 1 return primes[-1]Let's test the function with different values of n:
print(find_nth_prime(1)) # Output: 2 print(find_nth_prime(10)) # Output: 29 print(find_nth_prime(100)) # Output: 541The function correctly returns the 1st, 10th, and 100th prime numbers.
Potential improvements and alternative methods:
Sieve of Eratosthenes: Instead of checking each number individually, we can use the Sieve of Eratosthenes algorithm to generate prime numbers more efficiently. This algorithm marks all multiples of prime numbers as composite, leaving only the prime numbers unmarked.
Memoization: We can store the previously calculated prime numbers in a list or dictionary to avoid redundant calculations. This can improve the efficiency of the function when finding multiple prime numbers.
Wheel factorization: We can optimize the prime number generation by skipping numbers that are multiples of small prime numbers (e.g., 2, 3, 5). This reduces the number of iterations required to find prime numbers.
Probabilistic primality tests: For very large numbers, deterministic primality tests like the one used in the is_prime function can be slow. Probabilistic primality tests, such as the Miller-Rabin test, can provide faster results with a high probability of correctness.
These improvements and alternative methods can enhance the efficiency of finding prime numbers, especially for large values of n.
GPT-4 Turbo
Pseudocode:1. Define a function that takes in an integer n, which represents the nth prime number to find. 2. If n is less than or equal to 0, return an error message indicating n must be greater than 0. 3. Initialize a list to store prime numbers found (e.g., primes = []). 4. Start a loop to find prime numbers, beginning with the first prime number, 2. a. For each number, check if it is prime. i. A number is prime if it is not divisible evenly by any other number except 1 and itself. ii. To check if a number is prime, iterate through the list of already found prime numbers. - For each prime number in the list, if the current number is divisible by it, it's not a prime. b. If the number is prime, add it to the list of primes. c. If the length of the primes list is equal to n, break the loop because we've found the nth prime. 5. Return the last prime number added to the list, which represents the nth prime number found.Python Function:
def nth_prime(n): if n <= 0: return "Error: n must be greater than 0." primes = [] number = 2 # The first prime number while len(primes) < n: is_prime = True for prime in primes: if number % prime == 0: is_prime = False break if is_prime: primes.append(number) number += 1 return primes[-1] # Testing the function with three different values of n print(nth_prime(1)) # Output: 2 print(nth_prime(10)) # Output: 29 print(nth_prime(100)) # Output: 541Discussion:
The function correctly identifies the 1st, 10th, and 100th prime numbers, demonstrating its effectiveness and correctness. The algorithm uses a simple method of checking each number sequentially to see if it is prime and keeps a running tally of primes found until it reaches the nth prime.
Potential Improvements:
Performance Optimization: For larger values of n, the above algorithm may become inefficient due to its simplicity. One way to improve it could be to implement a more efficient prime-checking method, such as the Sieve of Eratosthenes for generating prime numbers up to a certain limit.
Checking up to the Square Root: In the prime-checking part, it is sufficient to check divisibility up to the square root of the current number instead of checking against all previously found prime numbers. This optimization could significantly reduce the number of checks for larger numbers.
Memoization: If the function will be called multiple times, storing previously calculated prime numbers outside the function and reusing them can save computation time.
Alternative Methods:
Sieve of Eratosthenes: This ancient algorithm is an efficient way to find all primes smaller than a specified integer. Adapting it to find the nth prime would involve generating primes in chunks until the nth prime is reached.
Segmented Sieve: A variation of the Sieve of Eratosthenes, useful for finding primes in a smaller segment, taking advantage of the fact that we can generate large primes without finding all the previous smaller primes.
Winner: Claude
Again, the code produced by both models runs well without modification. However, Claude tops GPT-4 again here in a couple ways:
Claude’s pseudocode is much easier to follow, and also provides a helpful explanation in natural language.
Claude baked in square root optimization, which is only mentioned by GPT-4.
Claude suggests an additional improvement, probabilistic primality tests, which GPT-4 doesn’t mention at all.
Claude’s response here is more natural, more complete, and shows a better understanding of the starting prompt.
After narrowly beating out Claude 3 last week, GPT-4 Turbo falters a bit with our more advanced tests. While both models produce technically correct responses, Claude shows a better understanding of the user’s need, demonstrating a more advanced logic process when it comes to highly technical questions.
For our tests its clear: Claude wins! But is that the case for every interaction? Comment below with your experiences and let us know if our conclusion here is solid.