diff --git a/src/rag.py b/src/rag.py
index 0f36934..f011f4e 100644
--- a/src/rag.py
+++ b/src/rag.py
@@ -1,4 +1,4 @@
-from typing import Any, List
+from typing import Any, List, Tuple, Optional
 from uuid import uuid4
 
 import adalflow as adal
@@ -16,6 +16,7 @@
 from config import configs
 from src.data_pipeline import DatabaseManager
 from adalflow.utils import printc
+from dataclasses import dataclass, field
 
 
 class Memory(DataComponent):
@@ -26,7 +27,6 @@ def __init__(self):
         self.current_conversation = Conversation()
 
     def call(self) -> List[DialogTurn]:
-
         all_dialog_turns = self.current_conversation.dialog_turns
 
         return all_dialog_turns
@@ -41,6 +41,14 @@ def add_dialog_turn(self, user_query: str, assistant_response: str):
         self.current_conversation.append_dialog_turn(dialog_turn)
 
 
+@dataclass
+class RAGAnswer(adal.DataClass):
+    rationale: str = field(default="", metadata={"desc": "Rationale for the answer."})
+    answer: str = field(default="", metadata={"desc": "Answer to the user query."})
+
+    __output_fields__ = ["rationale", "answer"]
+
+
 system_prompt = r"""
 You are a code assistant which answer's user question on a Github Repo. 
 You will receive user query, relevant context, and past conversation history.
@@ -75,23 +83,12 @@ def add_dialog_turn(self, user_query: str, assistant_response: str):
 <END_OF_USER_PROMPT>
 """
 
-from dataclasses import dataclass, field
-
-
-@dataclass
-class RAGAnswer(adal.DataClass):
-    rationale: str = field(default="", metadata={"desc": "Rationale for the answer."})
-    answer: str = field(default="", metadata={"desc": "Answer to the user query."})
-
-    __output_fields__ = ["rationale", "answer"]
-
 
 class RAG(adal.Component):
     __doc__ = """RAG with one repo.
     If you want to load a new repo. You need to call prepare_retriever(repo_url_or_path) first."""
 
     def __init__(self):
-
         super().__init__()
 
         # Initialize embedder, generator, and db_manager
@@ -119,6 +116,7 @@ def __init__(self):
             model_kwargs=configs["generator"]["model_kwargs"],
             output_processors=data_parser,
         )
+        self.previous_retrieved_documents = None
 
     def initialize_db_manager(self):
         self.db_manager = DatabaseManager()
@@ -136,15 +134,59 @@ def prepare_retriever(self, repo_url_or_path: str):
             document_map_func=lambda doc: doc.vector,
         )
 
-    def call(self, query: str) -> Any:
+    def is_clarification_query(self, query: str) -> bool:
+        """
+        Determines if the current query is a clarification of a previous query.
+        """
+        if not self.memory():
+            return False
+
+        clarification_prompt = f"""
+        You are a clarification detector. Analyze if the query is a follow-up or clarification of the previous conversation.
+        Your response should include:
+        - A rationale explaining your reasoning
+        - A clear True/False answer
+
+        Output your response in this format:
+            {{
+            "rationale": "Your step-by-step reasoning here",
+            "answer": "True or False"
+            }}
+
+        Conversation History:
+        {self.memory()}
+
+        Query:
+        {query}
+        """
+        response = self.generator(
+            prompt_kwargs={
+                "conversation_history": self.memory(),
+                "system_prompt": clarification_prompt,
+            },
+        )
 
-        retrieved_documents = self.retriever(query)
+        is_clarification = "true" in response.data.answer.lower()
+        return is_clarification
+
+    def call(self, query: str) -> Tuple[Any, Any]:
+        previous_context = (
+            self.previous_retrieved_documents[0].documents
+            if self.previous_retrieved_documents
+            else None
+        )
 
-        # fill in the document
-        retrieved_documents[0].documents = [
-            self.transformed_docs[doc_index]
-            for doc_index in retrieved_documents[0].doc_indices
-        ]
+        is_clarification = self.is_clarification_query(query)
+
+        if is_clarification and self.previous_retrieved_documents:
+            retrieved_documents = self.previous_retrieved_documents
+        else:
+            retrieved_documents = self.retriever(query)
+            retrieved_documents[0].documents = [
+                self.transformed_docs[doc_index]
+                for doc_index in retrieved_documents[0].doc_indices
+            ]
+            self.previous_retrieved_documents = retrieved_documents
 
         printc(f"retrieved_documents: {retrieved_documents[0].documents}")
         printc(f"memory: {self.memory()}")
@@ -168,6 +210,12 @@ def call(self, query: str) -> Any:
 
         return final_response, retrieved_documents
 
+    def _format_doc_paths(self, documents: List[Any]) -> str:
+        """Helper to format document paths for logging"""
+        return "\n  ".join(
+            f"- {doc.meta_data.get('file_path', 'unknown')}" for doc in documents
+        )
+
 
 if __name__ == "__main__":
     from adalflow.utils import get_logger