chore: prompt improved

Backend/app/api/v1/endpoints/prompts.py

@@ -9,15 +9,16 @@ Your task is to generate a batch of 10 high-quality MCQ questions strictly based
 -----------------------
 GENERATION RULES
 -----------------------
-1. Generate exactly 10 MCQs.
-2. Use only information from the provided inputs.
-3. Each question must be unambiguous, factual, and supported by the given data.
-4. Each MCQ MUST have exactly four options.
-5. Only one correct answer is allowed.
-6. Explanations must be short and directly justify the answer.
-7. `User_response` must ALWAYS remain an empty string.
-8. Output MUST be a valid JSON array containing 10 objects.
-9. Output MUST contain nothing except the JSON array (no commentary or markdown).
+1. Strictly follow the user_prompt instructions without deviation.
+2. Generate exactly 20 MCQs.
+3. Use only information from the provided inputs.
+4. Each question must be unambiguous, factual, and supported by the given data.
+5. Each MCQ MUST have exactly four options.
+6. Only one correct answer is allowed.
+7. Explanations must be short and directly justify the answer.
+8. `User_response` must ALWAYS remain an empty string.
+9. Output MUST be a valid JSON array containing 10 objects.
+10. Output MUST contain nothing except the JSON array (no commentary or markdown).
 
 -----------------------
 REQUIRED JSON FORMAT FOR EACH QUESTION
@@ -44,4 +45,4 @@ ANSWER KEY RULES
 - 'd' -> options[3]
 
 Strictly follow the JSON structure and generate exactly 10 MCQs.
-"""
+"""

Backend/app/api/v1/endpoints/quiz.py

@@ -151,8 +151,8 @@ async def generate_quiz_notes(
 
 async def prompt_builder(parsed_doc:str, user_prompt:str, docs:str=None):
     prompt = SYSTEM_PROMPT.format(
-        parsed_info=parsed_doc,
         user_prompt=user_prompt,
+        parsed_info=parsed_doc,
         retrieved_docs=docs
     )
     return prompt

Backend/app/llm.py

@@ -22,7 +22,7 @@ async def call_llm(prompt:str):
             ],
             # Use the OpenAI parameter to request JSON output
             response_format={"type": "json_object"}, 
-            temperature=0.7,
+            temperature=0.4,
         )
 
         json_string = response.choices[0].message.content