now using 4.1mini with temperature 1

core.py

@@ -91,7 +91,7 @@ def generate_synthetic_personas(num_personas: int, audience: str, previous_perso
             prompt += build_previous_personas_context(current_context_personas) # Appends the formatted list
 
         try:
-            response_str = call_llm(prompt=prompt, response_format=response_format,temperature=0.5, model="gpt-4.1-nano")
+            response_str = call_llm(prompt=prompt, response_format=response_format,temperature=1, model="gpt-4.1-mini")
             response_data = json.loads(response_str)
             users_list = response_data.get("users_personas", [])
 
@@ -169,7 +169,7 @@ def ask_all_questions_to_persona(persona: dict, questions: List[str]) -> str:
             questions=questions,
             num_questions=len(questions)
         )
-        response_str = call_llm(prompt=prompt,temperature=0.5, model="gpt-4o-mini",response_format=response_format)
+        response_str = call_llm(prompt=prompt,temperature=1, model="gpt-4.1-mini",response_format=response_format)
         response_data = json.loads(response_str)
         answers = response_data.get("answers", [])
         return answers
@@ -315,7 +315,7 @@ def chat_with_persona(persona: dict, question: str, conversation_history: List[d
     )
     if conversation_history:
         prompt += f"\nHere you have the previous conversation, make sure to answer the question in a way that is consistent with it:\n{history_context}"
-    return call_llm(prompt=prompt,temperature=0.5, model="gpt-4o-mini")
+    return call_llm(prompt=prompt,temperature=1, model="gpt-4.1-mini")
 
 def chat_with_report(users: List[dict], question: str, questions: List[str]) -> str:
     """

eval/main.py

@@ -116,7 +116,7 @@ def generate_users_batch(
             api_url,
             headers=headers,
             json=request_body,
-            timeout=180
+            timeout=600
         )
         response.raise_for_status()
         duration = time.time() - start_time

prompts.py

@@ -173,6 +173,7 @@ You are to fully embody and respond as the detailed user persona provided below.
 6.  **Crucial Prohibition:**
     * **DO NOT** start your answer with phrases like "As [Name of Persona]...", "As this persona...", "Based on my profile...", "As an AI language model...", or any other meta-references to being a persona or AI. Speak directly *as* the persona in the first person.
 
+Focus on the question asked, do not be biased by the persona's profile.
 Think carefully: Given everything you know about this individual – their identity, their job, their past, their personality, their core values, and their communication habits – how would they genuinely and thoughtfully respond to the question, sounding like the unique human they are meant to represent?
 """
 
@@ -250,6 +251,7 @@ Make sure your response is well-structured and professional.
 
 If the question is not relevant to the interview data, please say that you cannot answer it based on the provided information.
 Answer in the same language of the question.
+Put your focus on the question asked, do not be biased by the interview data.
 """
 
 # Prompt for generating audience name

utils.py

@@ -2,20 +2,28 @@ import openai
 
 LLM_MODEL = "gpt-4.1-nano"
 temperature = 0.5
+frequency_penalty=0
+presence_penalty=0
+top_p=0
 
-def call_llm(prompt: str, response_format=None, model=LLM_MODEL,temperature=temperature) -> str:
+def call_llm(prompt: str, response_format=None, model=LLM_MODEL,temperature=temperature,frequency_penalty=frequency_penalty,presence_penalty=presence_penalty,top_p=top_p) -> str:
     client = openai.OpenAI()
     if response_format:
         response = client.beta.chat.completions.parse(
             model=model,
             messages=[{"role": "user", "content": prompt}],
             response_format=response_format,
-            temperature=temperature
+            temperature=temperature,
+            frequency_penalty=frequency_penalty,
+            presence_penalty=presence_penalty,
+            top_p=top_p
         )
     else:
         response = client.chat.completions.create(
             model=model,
             messages=[{"role": "user", "content": prompt}],
-            temperature=temperature
+            temperature=temperature,
+            frequency_penalty=0.0,
+            presence_penalty=0.0
         )
     return response.choices[0].message.content