diff --git a/pandasai/prompts/generate_python_code.py b/pandasai/prompts/generate_python_code.py
index bff66e142..902f2c841 100644
--- a/pandasai/prompts/generate_python_code.py
+++ b/pandasai/prompts/generate_python_code.py
@@ -51,7 +51,7 @@ class GeneratePythonCodePrompt(Prompt):
 # Analyze the data
 # 1. Prepare: Preprocessing and cleaning data if necessary
 # 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
-# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in exports/charts/temp_chart.png and do not show the chart.)
+# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in {save_charts_path}/temp_chart.png and do not show the chart.)
 # 4. Output: return a dictionary of:
 # - type (possible values "text", "number", "dataframe", "plot")
 # - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
diff --git a/pandasai/smart_datalake/__init__.py b/pandasai/smart_datalake/__init__.py
index 8a6d612b5..bcc53e9b2 100644
--- a/pandasai/smart_datalake/__init__.py
+++ b/pandasai/smart_datalake/__init__.py
@@ -262,6 +262,7 @@ def chat(self, query: str):
                 default_values = {
                     # TODO: find a better way to determine the engine,
                     "engine": self._dfs[0].engine,
+                    "save_charts_path": self._config.save_charts_path.rstrip("/"),
                 }
                 generate_python_code_instruction = self._get_prompt(
                     "generate_python_code",
diff --git a/tests/prompts/test_generate_python_code_prompt.py b/tests/prompts/test_generate_python_code_prompt.py
index e5597ba14..889723ddf 100644
--- a/tests/prompts/test_generate_python_code_prompt.py
+++ b/tests/prompts/test_generate_python_code_prompt.py
@@ -23,6 +23,7 @@ def test_str_with_args(self):
         prompt = GeneratePythonCodePrompt()
         prompt.set_var("dfs", dfs)
         prompt.set_var("conversation", "Question")
+        prompt.set_var("save_charts_path", "exports/charts")
         assert (
             prompt.to_string()
             == """
@@ -51,6 +52,61 @@ def analyze_data(dfs: list[pd.DataFrame]) -> dict:
    # Code goes here (do not add comments)
     
 
+# Declare a result variable
+result = analyze_data(dfs)
+```
+
+Using the provided dataframes (`dfs`), update the python code based on the last user question:
+Question
+
+Updated code:
+"""  # noqa: E501
+        )
+
+    def test_str_with_custom_save_charts_path(self):
+        """Test that the __str__ method is implemented"""
+
+        llm = FakeLLM("plt.show()")
+        dfs = [
+            SmartDataframe(
+                pd.DataFrame({"a": [1], "b": [4]}),
+                config={"llm": llm},
+            )
+        ]
+
+        prompt = GeneratePythonCodePrompt()
+        prompt.set_var("dfs", dfs)
+        prompt.set_var("conversation", "Question")
+        prompt.set_var("save_charts_path", "custom_path")
+
+        assert (
+            prompt.to_string()
+            == """
+You are provided with the following pandas DataFrames with the following metadata:
+
+Dataframe dfs[0], with 1 rows and 2 columns.
+This is the metadata of the dataframe dfs[0]:
+a,b
+1,4
+
+
+This is the initial python code to be updated:
+```python
+# TODO import all the dependencies required
+import pandas as pd
+
+# Analyze the data
+# 1. Prepare: Preprocessing and cleaning data if necessary
+# 2. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.)
+# 3. Analyze: Conducting the actual analysis (if the user asks to create a chart save it to an image in custom_path/temp_chart.png and do not show the chart.)
+# 4. Output: return a dictionary of:
+# - type (possible values "text", "number", "dataframe", "plot")
+# - value (can be a string, a dataframe or the path of the plot, NOT a dictionary)
+# Example output: { "type": "text", "value": "The average loan amount is $15,000." }
+def analyze_data(dfs: list[pd.DataFrame]) -> dict:
+   # Code goes here (do not add comments)
+    
+
 # Declare a result variable
 result = analyze_data(dfs)
 ```
diff --git a/tests/test_smartdataframe.py b/tests/test_smartdataframe.py
index 37fa88f88..e5bf6d823 100644
--- a/tests/test_smartdataframe.py
+++ b/tests/test_smartdataframe.py
@@ -240,6 +240,71 @@ def test_getters_are_accessible(self, smart_dataframe: SmartDataframe, llm):
             == "def analyze_data(dfs):\n    return {'type': 'number', 'value': 1}"
         )
 
+    def test_save_chart_non_default_dir(
+        self, smart_dataframe: SmartDataframe, llm, sample_df
+    ):
+        """
+        Test chat with `SmartDataframe` with custom `save_charts_path`.
+
+        Script:
+            1) Ask `SmartDataframe` to build a chart and save it in
+               a custom directory;
+            2) Check if substring representing the directory present in
+               `llm.last_prompt`.
+            3) Check if the code has had a call of `plt.savefig()` passing
+               the custom directory.
+
+        Notes:
+            1) Mock `import_dependency()` util-function to avoid the
+               actual calls to `matplotlib.pyplot`.
+            2) The `analyze_data()` function in the code fixture must have
+               `"type": None` in the result dict. Otherwise, if it had
+               `"type": "plot"` (like it has in practice), `_format_results()`
+               method from `SmartDatalake` object would try to read the image
+               with `matplotlib.image.imread()` and this test would fail.
+               Those calls to `matplotlib.image` are unmockable because of
+               imports inside the function scope, not in the top of a module.
+               @TODO: figure out if we can just move the imports beyond to
+                      make it possible to mock out `matplotlib.image`
+        """
+        llm._output = """
+import pandas as pd
+import matplotlib.pyplot as plt
+def analyze_data(dfs: list[pd.DataFrame]) -> dict:
+    df = dfs[0].nlargest(5, 'happiness_index')
+    
+    plt.figure(figsize=(8, 6))
+    plt.pie(df['happiness_index'], labels=df['country'], autopct='%1.1f%%')
+    plt.title('Happiness Index for the 5 Happiest Countries')
+    plt.savefig('custom-dir/output_charts/temp_chart.png')
+    plt.close()
+    
+    return {"type": None, "value": "custom-dir/output_charts/temp_chart.png"}
+result = analyze_data(dfs)
+"""
+        with patch(
+            "pandasai.helpers.code_manager.import_dependency"
+        ) as import_dependency_mock:
+            smart_dataframe = SmartDataframe(
+                sample_df,
+                config={
+                    "llm": llm,
+                    "enable_cache": False,
+                    "save_charts": True,
+                    "save_charts_path": "custom-dir/output_charts/",
+                },
+            )
+
+            smart_dataframe.chat("Plot pie-chart the 5 happiest countries")
+
+        assert "custom-dir/output_charts/temp_chart.png" in llm.last_prompt
+        plt_mock = getattr(import_dependency_mock.return_value, "matplotlib.pyplot")
+        assert plt_mock.savefig.called
+        assert (
+            plt_mock.savefig.call_args.args[0]
+            == "custom-dir/output_charts/temp_chart.png"
+        )
+
     def test_add_middlewares(self, smart_dataframe: SmartDataframe, custom_middleware):
         middleware = custom_middleware()
         smart_dataframe.add_middlewares(middleware)