diff --git a/examples/data_/BartoSutton.pdf b/examples/data_/BartoSutton.pdf
deleted file mode 100644
index 9deb811..0000000
Binary files a/examples/data_/BartoSutton.pdf and /dev/null differ
diff --git a/examples/main_cli_example.py b/examples/main_cli_example.py
index e89e1ee..a03035c 100644
--- a/examples/main_cli_example.py
+++ b/examples/main_cli_example.py
@@ -72,6 +72,7 @@ async def main():
     chat = LeannChat(index_path=INDEX_PATH)
     
     query = "Based on the paper, what are the main techniques LEANN explores to reduce the storage overhead and DLPM explore to achieve Fairness and Efiiciency trade-off?"
+    query = "What is the main idea of RL and give me 5 exapmle of classic RL algorithms?"
     print(f"You: {query}")
     chat_response = chat.ask(query, top_k=20, recompute_beighbor_embeddings=True)
     print(f"Leann: {chat_response}")
diff --git a/examples/resue_index.py b/examples/resue_index.py
new file mode 100644
index 0000000..6e7dbfc
--- /dev/null
+++ b/examples/resue_index.py
@@ -0,0 +1,18 @@
+import asyncio
+from leann.api import LeannChat
+from pathlib import Path
+
+INDEX_DIR = Path("./test_pdf_index")
+INDEX_PATH = str(INDEX_DIR / "pdf_documents.leann")
+
+async def main():
+    print(f"\n[PHASE 2] Starting Leann chat session...")
+    chat = LeannChat(index_path=INDEX_PATH)
+    query = "What is the main idea of RL and give me 5 exapmle of classic RL algorithms?"
+    query = "Based on the paper, what are the main techniques LEANN explores to reduce the storage overhead and DLPM explore to achieve Fairness and Efiiciency trade-off?"
+
+    response = chat.ask(query,top_k=20,recompute_beighbor_embeddings=True,complexity=64,beam_width=1)
+    print(f"\n[PHASE 2] Response: {response}")
+
+if __name__ == "__main__":
+    asyncio.run(main())
\ No newline at end of file
diff --git a/packages/leann-core/src/leann/api.py b/packages/leann-core/src/leann/api.py
index 48478bf..014264a 100644
--- a/packages/leann-core/src/leann/api.py
+++ b/packages/leann-core/src/leann/api.py
@@ -217,7 +217,12 @@ class LeannChat:
         results = self.searcher.search(question, top_k=top_k, **kwargs)
         context = "\n\n".join([r.text for r in results])
 
-        prompt = f"Context:\n{context}\n\nQuestion: {question}\n\nAnswer:"
+        prompt = (
+            "Here is some retrieved context that might help answer your question:\n\n"
+            f"{context}\n\n"
+            f"Question: {question}\n\n"
+            "Please provide the best answer you can based on this context and your knowledge."
+        )
 
         print(f"DEBUG: Calling LLM with prompt: {prompt}...")
         try: