diff --git a/app/services/llm_service.py b/app/services/llm_service.py
index 09665d5..21dff82 100644
--- a/app/services/llm_service.py
+++ b/app/services/llm_service.py
@@ -41,7 +41,7 @@ class OpenAIService(LLMService):
         except (ImportError, AttributeError) as e:
             logger.error(f"Failed to initialize OpenAI service: {e}")
             raise RuntimeError(f"OpenAI service initialization failed: {e}")
-    
+
     async def chat_to_tasks(self, prompt: str) -> List[Dict]:
         """
         Convert natural language to tasks using OpenAI.
@@ -63,7 +63,7 @@ class OpenAIService(LLMService):
         
         Return ONLY a JSON array of task objects without any additional text or explanation.
         """
-        
+
         try:
             response = await self.client.chat.completions.create(
                 model=self.model,
@@ -74,16 +74,14 @@ class OpenAIService(LLMService):
                 response_format={"type": "json_object"},
                 temperature=0.2,
             )
-            
-            # Extract the JSON content from the response
+
             content = response.choices[0].message.content
             result = json.loads(content)
-            
-            # Expect a "tasks" key in the response JSON
+
             if "tasks" in result:
                 return result["tasks"]
-            return [result]  # Return as a single-item list if no "tasks" key
-            
+            return [result]
+
         except Exception as e:
             logger.error(f"OpenAI API error: {e}")
             raise RuntimeError(f"Failed to process request with OpenAI: {e}")
@@ -91,7 +89,7 @@ class OpenAIService(LLMService):
 
 class GeminiService(LLMService):
     """Google Gemini implementation of LLM service."""
-    
+
     def __init__(self):
         """Initialize the Gemini service."""
         try:
@@ -101,56 +99,62 @@ class GeminiService(LLMService):
         except (ImportError, AttributeError) as e:
             logger.error(f"Failed to initialize Gemini service: {e}")
             raise RuntimeError(f"Gemini service initialization failed: {e}")
-    
+
     async def chat_to_tasks(self, prompt: str) -> List[Dict]:
         """
         Convert natural language to tasks using Google Gemini.
-        
+
         Args:
             prompt: User's natural language input
-            
+
         Returns:
             List of task dictionaries
         """
-        system_prompt = """
-        You are a task extraction assistant. Your job is to convert the user's natural language 
-        input into one or more structured task objects. Each task should have these properties:
-        - title: A short, clear title for the task
-        - description: A more detailed description of what needs to be done
-        - due_date: When the task is due (ISO format date string, or null if not specified)
-        - priority: The priority level (high, medium, low)
-        - status: The status (defaults to "pending")
-        
-        Return ONLY a JSON object with a "tasks" key that contains an array of task objects.
-        Do not include any text or explanation outside the JSON.
-        """
-        
+        system_prompt = (
+            "You are a task extraction assistant. Your job is to convert the user's natural language "
+            "input into one or more structured task objects. Each task should have these properties:\n"
+            "- title: A short, clear title for the task\n"
+            "- description: A more detailed description of what needs to be done\n"
+            "- due_date: When the task is due (ISO format date string, or null if not specified)\n"
+            "- priority: The priority level (high, medium, low)\n"
+            "- status: The status (defaults to \"pending\")\n\n"
+            "Return ONLY a JSON object with a \"tasks\" key that contains an array of task objects. "
+            "Do not include any text or explanation outside the JSON."
+        )
+
         try:
-            # Start the chat session with the system prompt
             chat = self.model.start_chat(history=[
-                {"role": "system", "content": system_prompt}
+                {
+                    "content": {
+                        "parts": [system_prompt]
+                    }
+                }
             ])
-            
-            # Send the user prompt asynchronously
-            response = await chat.send_message_async(prompt)
+
+            response = await chat.send_message_async(
+                {
+                    "content": {
+                        "parts": [prompt]
+                    }
+                }
+            )
+
             content = response.text
-            
-            # Remove possible markdown code blocks wrapping the JSON response
+
+            # Remove possible markdown code blocks
             if "```json" in content:
                 json_str = content.split("```json")[1].split("```")[0].strip()
             elif "```" in content:
                 json_str = content.split("```")[1].strip()
             else:
                 json_str = content.strip()
-            
-            # Parse JSON response
+
             result = json.loads(json_str)
-            
-            # Return the list under "tasks" or the whole as single-item list
+
             if "tasks" in result:
                 return result["tasks"]
             return [result]
-            
+
         except Exception as e:
             logger.error(f"Gemini API error: {e}")
             raise RuntimeError(f"Failed to process request with Gemini: {e}")
@@ -158,25 +162,25 @@ class GeminiService(LLMService):
 
 class MockLLMService(LLMService):
     """Mock LLM service for testing."""
-    
+
     async def chat_to_tasks(self, prompt: str) -> List[Dict]:
         """
         Return mock tasks based on the prompt.
-        
+
         Args:
             prompt: User's natural language input
-            
+
         Returns:
             List of task dictionaries
         """
         words = prompt.lower().split()
         priority = "medium"
-        
+
         if "urgent" in words or "important" in words:
             priority = "high"
         elif "low" in words or "minor" in words:
             priority = "low"
-            
+
         return [{
             "title": prompt[:50] + ("..." if len(prompt) > 50 else ""),
             "description": prompt,
@@ -189,7 +193,7 @@ class MockLLMService(LLMService):
 def get_llm_service() -> LLMService:
     """
     Factory function for LLM service dependency injection.
-    
+
     Returns:
         An instance of a concrete LLMService implementation
     """