diff --git a/textgrad/model.py b/textgrad/model.py
index 882cf19..4e71d78 100644
--- a/textgrad/model.py
+++ b/textgrad/model.py
@@ -1,10 +1,8 @@
-from typing import List, Union
-from functools import partial
+from typing import Union
 from textgrad.variable import Variable
 from textgrad.autograd import LLMCall
 from textgrad.autograd.function import Module
 from textgrad.engine import EngineLM, get_engine
-from textgrad.autograd.llm_ops import FormattedLLMCall
 from .config import SingletonBackwardEngine
 
 class BlackboxLLM(Module):
diff --git a/textgrad/optimizer/optimizer.py b/textgrad/optimizer/optimizer.py
index f3e3d8e..5ebec04 100644
--- a/textgrad/optimizer/optimizer.py
+++ b/textgrad/optimizer/optimizer.py
@@ -177,7 +177,13 @@ def step(self):
             prompt_update_parameter = self._update_prompt(parameter)
             new_text = self.engine(prompt_update_parameter, system_prompt=self.optimizer_system_prompt)
             logger.info(f"TextualGradientDescent optimizer response", extra={"optimizer.response": new_text})
-            parameter.set_value(new_text.split(self.new_variable_tags[0])[1].split(self.new_variable_tags[1])[0].strip())
+            try:
+                new_value = new_text.split(self.new_variable_tags[0])[1].split(self.new_variable_tags[1])[0].strip()
+            # Check if we got a cannot be indexed error
+            except IndexError:
+                logger.error(f"TextualGradientDescent optimizer response could not be indexed", extra={"optimizer.response": new_text})
+                raise IndexError(f"TextualGradientDescent optimizer response could not be indexed. This can happen if the optimizer model cannot follow the instructions. You can try using a stronger model, or somehow reducing the context of the optimization. Response: {new_text}")
+            parameter.set_value(new_value)
             logger.info(f"TextualGradientDescent updated text", extra={"parameter.value": parameter.value})
             if self.verbose:
                 print("-----------------------TextualGradientDescent------------------------")
@@ -263,5 +269,12 @@ def step(self):
             prompt_update_parameter = self._update_prompt(parameter, momentum_storage_idx=idx)
             new_text = self.engine(prompt_update_parameter, system_prompt=self.optimizer_system_prompt)
             logger.info(f"TextualGradientDescentwithMomentum optimizer response", extra={"optimizer.response": new_text})
-            parameter.set_value(new_text.split(self.new_variable_tags[0])[1].split(self.new_variable_tags[1])[0].strip())
+            try:
+                new_value = new_text.split(self.new_variable_tags[0])[1].split(self.new_variable_tags[1])[0].strip()
+            # Check if we got a cannot be indexed error
+            except IndexError:
+                logger.error(f"TextualGradientDescent optimizer response could not be indexed", extra={"optimizer.response": new_text})
+                raise IndexError(f"TextualGradientDescent optimizer response could not be indexed. This can happen if the optimizer model cannot follow the instructions. You can try using a stronger model, or somehow reducing the context of the optimization. Response: {new_text}")
+            parameter.set_value(new_value)
+            logger.info(f"TextualGradientDescent updated text", extra={"parameter.value": parameter.value})            
             logger.info(f"TextualGradientDescentwithMomentum updated text", extra={"parameter.value": parameter.value})