Getting the perfect AI response rarely happens on the first try. Iteration—refining your prompts based on the AI's output—is key. Treat it like a conversation, using Cursor's Chat or Inline Edit features.
You (Initial Prompt):
Write a Python function to get data from `https://api.example.com/data`
Cursor (Potential Initial Response): (Generates code using `requests` library)
import requests
def get_data():
response = requests.get('https://api.example.com/data')
response.raise_for_status() # Raise an exception for bad status codes
return response.json()
You (Analysis): Good start, but I need to use the standard `urllib.request` and handle potential `JSONDecodeError`.
You (Follow-up Prompt in same Chat):
Thanks. Please rewrite that function using only the standard `urllib.request` library and add a try-except block to handle potential JSONDecodeError, returning None in case of an error.
Cursor (Refined Response): (Generates code using `urllib` and error handling)
import urllib.request
import json
def get_data():
try:
with urllib.request.urlopen('https://api.example.com/data') as response:
if response.status == 200:
data = response.read()
try:
return json.loads(data)
except json.JSONDecodeError:
print("Error decoding JSON")
return None
else:
print(f"HTTP Error: {response.status}")
return None
except urllib.error.URLError as e:
print(f"URL Error: {e.reason}")
return None
except Exception as e:
print(f"An error occurred: {e}")
return None
Don't expect perfection immediately. Analyze Cursor's output critically and use precise follow-up prompts in Chat or via Cmd+K to correct, refine, and guide it towards the desired solution. Use the chat history or select generated code to maintain context during iteration.