1. Modified the query prompt to explicitly ask for differentiating questions

2. Started with lower confidence (0.6) to reflect initial uncertainty
3. Used the query response to guide specific follow-up questions
4. Removed generic timing questions
5. Added structure to parse multiple potential diagnoses

src/app.py

@@ -145,16 +145,20 @@ def process_speech(new_transcript, history):
     
     try:
         # Query the symptom index with user's transcript
-        response = symptom_index.as_query_engine().query(new_transcript)
+        response = symptom_index.as_query_engine().query(
+            f"""Based on this symptom description, list the top 2-3 most likely ICD-10 codes 
+            and key differentiating questions we should ask: {new_transcript}"""
+        )
         
-        # Extract the ICD code and description
-        icd_info = str(response).strip()
+        # Parse the response and structure initial findings
+        icd_codes = str(response).strip().split('\n')
+        primary_diagnosis = icd_codes[0] if icd_codes else "Unknown"
         
-        # Structure the initial response
+        # Structure a response that includes specific follow-up
         formatted_response = {
-            "diagnoses": [icd_info],
-            "confidences": [0.8],  # We could make this more dynamic
-            "follow_up": f"Could you tell me more about your symptoms? When did they start?"
+            "diagnoses": [primary_diagnosis],
+            "confidences": [0.6],  # Start with moderate confidence
+            "follow_up": f"To differentiate between possible diagnoses, {icd_codes[1] if len(icd_codes) > 1 else 'please describe'}:"
         }
         
         # Add to conversation history
@@ -163,9 +167,9 @@ def process_speech(new_transcript, history):
         
     except Exception as e:
         error_response = {
-            "error": str(e), 
+            "error": str(e),
             "status": "error",
-            "transcript": new_transcript  # Include what we heard for debugging
+            "transcript": new_transcript
         }
         history.append({"role": "user", "content": new_transcript})
         history.append({"role": "assistant", "content": json.dumps(error_response, indent=2)})