mito-ai: update the production prompt

mito-ai/src/Extensions/AiChat/PromptManager.tsx

@@ -5,56 +5,59 @@ export function createBasicPrompt(
     activeCellCode: string,
     input: string
 ): string {
-    const prompt = `You have access to the following variables:
+    const prompt = `You are an expert python programmer writing a script in a Jupyter notebook. You are given a set of variables, existing code, and a task.
 
-${variables?.map(variable => `${JSON.stringify(variable, null, 2)}\n`).join('')}
-
-Complete the task below. Decide what variables to use and what changes you need to make to the active code cell. Only return the full new active code cell and a concise explanation of the changes you made.
-
-<Reminders>
-
-Do not: 
-- Use the word "I"
-- Include multiple approaches in your response
-- Recreate variables that already exist
+Respond with the updated active code cell and a short explanation of the changes you made.
 
-Do: 
-- Use the variables that you have access to
+When responding:
+- Do not use the word "I"
+- Do not recreate variables that already exist
 - Keep as much of the original code as possible
-- Ask for more context if you need it. 
-
-</Reminders>
 
 <Example>
 
-Code in the active code cell:
+Defined Variables:
+{{
+    'loan_multiplier': 1.5,
+    'sales_df': pd.DataFrame({{
+        'transaction_date': ['2024-01-02', '2024-01-02', '2024-01-02', '2024-01-02', '2024-01-03'],
+        'price_per_unit': [10, 9.99, 13.99, 21.00, 100],
+        'units_sold': [1, 2, 1, 4, 5],
+        'total_price': [10, 19.98, 13.99, 84.00, 500]
+    }})
+}}
 
+Code in the active code cell:
 \`\`\`python
 import pandas as pd
-loans_df = pd.read_csv('./loans.csv')
+sales_df = pd.read_csv('./sales.csv')
 \`\`\`
 
-Your task: convert the issue_date column to datetime.
+Your task: convert the transaction_date column to datetime and then multiply the total_price column by the sales_multiplier.
 
 Output:
 
 \`\`\`python
 import pandas as pd
-loans_df = pd.read_csv('./loans.csv')
-loans_df['issue_date'] = pd.to_datetime(loans_df['issue_date'])
+sales_df = pd.read_csv('./sales.csv')
+sales_df['transaction_date'] = pd.to_datetime(sales_df['transaction_date'])
+sales_df['total_price'] = sales_df['total_price'] * sales_multiplier
 \`\`\`
 
-Use the pd.to_datetime function to convert the issue_date column to datetime.
+Converted the \`transaction_date\` column to datetime using the built-in pd.to_datetime function and multiplied the \`total_price\` column by the \`sales_multiplier\` variable.
 
 </Example>
 
+Defined Variables:
+
+${variables?.map(variable => `${JSON.stringify(variable, null, 2)}\n`).join('')}
 Code in the active code cell:
 
 \`\`\`python
 ${activeCellCode}
 \`\`\`
 
-Your task: ${input}`;
+Your task: ${input}`
 
     console.log(prompt);