diff --git a/src/khoj/processor/conversation/gpt4all/chat_model.py b/src/khoj/processor/conversation/gpt4all/chat_model.py
index e02b8dfc..1047ac3b 100644
--- a/src/khoj/processor/conversation/gpt4all/chat_model.py
+++ b/src/khoj/processor/conversation/gpt4all/chat_model.py
@@ -125,7 +125,7 @@ def converse_offline(
     # Get Conversation Primer appropriate to Conversation Type
     # TODO If compiled_references_message is too long, we need to truncate it.
     if compiled_references_message == "":
-        conversation_primer = prompts.conversation_llamav2.format(query=user_query)
+        conversation_primer = user_query
     else:
         conversation_primer = prompts.notes_conversation_llamav2.format(
             query=user_query, references=compiled_references_message
diff --git a/src/khoj/processor/conversation/utils.py b/src/khoj/processor/conversation/utils.py
index b739217c..af21db0b 100644
--- a/src/khoj/processor/conversation/utils.py
+++ b/src/khoj/processor/conversation/utils.py
@@ -102,7 +102,7 @@ def generate_chatml_messages_with_context(
     return messages[::-1]
 
 
-def truncate_messages(messages, max_prompt_size, model_name):
+def truncate_messages(messages: list[ChatMessage], max_prompt_size, model_name) -> list[ChatMessage]:
     """Truncate messages to fit within max prompt size supported by model"""
 
     if "llama" in model_name:
@@ -110,24 +110,27 @@ def truncate_messages(messages, max_prompt_size, model_name):
     else:
         encoder = tiktoken.encoding_for_model(model_name)
 
+    system_message = messages.pop()
+    system_message_tokens = len(encoder.encode(system_message.content))
+
     tokens = sum([len(encoder.encode(message.content)) for message in messages])
-    while tokens > max_prompt_size and len(messages) > 1:
+    while (tokens + system_message_tokens) > max_prompt_size and len(messages) > 1:
         messages.pop()
         tokens = sum([len(encoder.encode(message.content)) for message in messages])
 
-    # Truncate last message if still over max supported prompt size by model
-    if tokens > max_prompt_size:
-        last_message = "\n".join(messages[-1].content.split("\n")[:-1])
-        original_question = "\n".join(messages[-1].content.split("\n")[-1:])
+    # Truncate current message if still over max supported prompt size by model
+    if (tokens + system_message_tokens) > max_prompt_size:
+        current_message = "\n".join(messages[0].content.split("\n")[:-1])
+        original_question = "\n".join(messages[0].content.split("\n")[-1:])
         original_question_tokens = len(encoder.encode(original_question))
         remaining_tokens = max_prompt_size - original_question_tokens
-        truncated_message = encoder.decode(encoder.encode(last_message)[:remaining_tokens]).strip()
+        truncated_message = encoder.decode(encoder.encode(current_message)[:remaining_tokens]).strip()
         logger.debug(
-            f"Truncate last message to fit within max prompt size of {max_prompt_size} supported by {model_name} model:\n {truncated_message}"
+            f"Truncate current message to fit within max prompt size of {max_prompt_size} supported by {model_name} model:\n {truncated_message}"
         )
-        messages = [ChatMessage(content=truncated_message + original_question, role=messages[-1].role)]
+        messages = [ChatMessage(content=truncated_message + original_question, role=messages[0].role)]
 
-    return messages
+    return messages + [system_message]
 
 
 def reciprocal_conversation_to_chatml(message_pair):