Update prompt

prompt.py

@@ -75,7 +75,7 @@ You will be provided with the first few rows of data below that represents the d
 Follow this process:
 
 1. **Observe the sample data.**
-2. Observe description and create a valid pander check
+2. Observe description and create a valid check
 
    Here are the valid **Pandera** Checks that you can use:
    1. 'pa.Check.between(min_value, max_value, include_min=True, include_max=True, **kwargs)'
@@ -101,20 +101,19 @@ Follow this process:
    21. 'pa.Check.str_startswith(string, **kwargs)' Checks if a string starts with the specified substring. 
    22. 'pa.Check.unique_values_eq(values, **kwargs)' Checks if the unique values in a column are equal to the specified set of values.
    23. 'pa.Check(lambda x: x )' with lambda functions for custom logic.
-   24. 'pa.Column(int, nullable=False, unique=True, name='column_name') For unqiue values
-   **ALWAY USE THE COMPLETE PANDERA SYNTAX
+   **ALWAY USE THE COMPLETE PANDERA SYNTAX**
 
 3. For each column, generate a **column name**, **rule name**, and a **Pandera rule** based on the user’s description. Example structure:
 
    ```json
    [
-     {
-       "column_name": "OS",
-       "rule_name": "Allowed Operating Systems",
-       "pandera_rule": "Column(str, pa.Check.isin(['macOS', 'Windows', 'Linux']), nullable=False, name='OS')"
-     }
+    {
+        "column_name": "unique_key",
+        "rule_name": "Unique Identifiers",
+        "pandera_rule": "pa.Column(int, nullable=False, unique=True, name='unique_key')"
+    }
    ]
-
+    
 4. Repeat this process for a maximum of 5 columns or based on user input. Group all the rules into a single JSON object and return it.
 IMPORTANT: You should only generate rules based on the user’s input for each column. Return the final rules as a single JSON object, ensuring that the user's instructions are reflected in the validations.