Improved all prompts

app.py

@@ -11,7 +11,6 @@ default_audio_params = {
     "editable": False,
     "container": False,
     "show_share_button": False,
-    "min_length": 5,
 }
 
 
@@ -118,7 +117,7 @@ with gr.Blocks() as demo:
         inputs=[chat],
         outputs=[chat],
     ).then(
-        fn=end_interview, inputs=[chat_history, model_select], outputs=feedback
+        fn=end_interview, inputs=[description, chat_history, model_select], outputs=feedback
     ).then(fn=hide_solution, inputs=None, outputs=[solution_acc, end_btn, problem_acc, audio_input])
 
     audio_input.stop_recording(fn=transcribe_audio, inputs=[audio_input], outputs=[message]).then(

llm.py

@@ -4,7 +4,7 @@ from dotenv import load_dotenv
 from openai import OpenAI
 
 from audio import numpy_audio_to_bytes
-from prompts import coding_interviewer_prompt, grading_feedback_prompt
+from prompts import coding_interviewer_prompt, grading_feedback_prompt, problem_generation_prompt
 
 load_dotenv()
 # TODO: don't use my key
@@ -20,33 +20,42 @@ def init_bot(problem=""):
 
 
 def get_problem(requirements, difficulty, topic, model, client=client):
-    prompt_system = "You are ChatGPT acting as a coding round interviewer for a big-tech company. "
-    full_prompt = f"Generate a {difficulty} {topic} problem in. Follow additional requirements: {requirements}. The problem should be solvable within 30 minutes."
+    full_prompt = (
+        f"Create a {difficulty} {topic} coding problem. "
+        f"Additional requirements: {requirements}. "
+        "The problem should be clearly stated, well-formatted, and solvable within 30 minutes. "
+        "Ensure the problem varies each time to provide a wide range of challenges."
+    )
     response = client.chat.completions.create(
         model=model,
         messages=[
-            {"role": "system", "content": prompt_system},
+            {"role": "system", "content": problem_generation_prompt},
             {"role": "user", "content": full_prompt},
         ],
-        temperature=1.5,
+        temperature=1.0,  # Adjusted for a balance between creativity and coherency
     )
     question = response.choices[0].message.content.strip()
     chat_history = init_bot(question)
     return question, chat_history
 
 
-def end_interview(chat_history, model, client=client):
+def end_interview(problem_description, chat_history, model, client=client):
+    if not chat_history or len(chat_history) <= 2:
+        return "No interview content available to review."
+
     transcript = []
     for message in chat_history[1:]:
         role = message["role"]
         content = f"{role.capitalize()}: {message['content']}"
         transcript.append(content)
+
     response = client.chat.completions.create(
         model=model,
         messages=[
             {"role": "system", "content": grading_feedback_prompt},
-            {"role": "user", "content": "Interview transcript:" + "\n\n".join(transcript)},
-            {"role": "user", "content": "Grade the interview based on the transcript provided and give a feedback."},
+            {"role": "user", "content": f"The original problem to solve: {problem_description}"},
+            {"role": "user", "content": "\n\n".join(transcript)},
+            {"role": "user", "content": "Grade the interview based on the transcript provided and give feedback."},
         ],
         temperature=0.5,
     )

options.py

@@ -28,7 +28,7 @@ models = ["gpt-3.5-turbo"]
 fixed_messages = {
     "intro": "Welcome to the coding interview! I am your AI interview assistant. For the start select the difficulty and topic of the problem you would like to solve. Then click on the 'Generate a problem' button. Good luck!",
     "start": (
-        "Please take a moment to read the problem statement. Then you can share you initial thoughts and ask any questions you may have. Good luck! "
+        "Please take a moment to read the problem statement. Then you can share you initial thoughts and ask any questions you may have. "
         "Please use the record button to communicate with me. Try to think out loud and record all you comments and questions as if it is the real interview."
     ),
     "end": "The interview has concluded. Thank you for your participation! In a moment I will provide a detailed feedback on your performance.",

prompts.py

@@ -1,24 +1,42 @@
+problem_generation_prompt = (
+    "You are AI acting as a coding round interviewer for a big-tech company. "
+    "Generate a problem that tests the candidate's ability to solve real-world coding challenges efficiently. "
+    "Ensure the problem tests for problem-solving skills, technical proficiency, code quality, and handling of edge cases. "
+)
+
 # Coding round interviewer instructions
 coding_interviewer_prompt = (
-    "As an AI acting as a coding interviewer for a major tech company, you are to maintain a strict demeanor. "
-    "Provide hints only when the candidate is noticeably stuck or explicitly requests assistance. "
-    "Allow candidates to identify and resolve errors independently. "
-    "Encourage the candidate to explore improved solutions through probing questions, delaying hints as needed. "
-    "Discuss the time and space complexity after each solution iteration, aiming for optimal outcomes. "
-    "Responses should be in JSON format with two fields: "
-    "1. 'reply_to_candidate': visible feedback to the candidate. "
-    "2. 'hidden_note': internal notes useful for grading, possibly including code snippets, identified errors, and key observations. "
-    "The 'hidden_note' may be omitted if there are no new critical insights. "
+    "As an AI acting as a coding interviewer for a major tech company, you are to maintain a professional and analytical demeanor. "
+    "You must consistently ask about the time and space complexity of the candidate's solutions after each significant problem-solving step. "
+    "Prompt the candidate to explain how they compute these complexities, and guide them through the process if necessary, without providing the answers directly. "
+    "Encourage thorough exploration of solutions without revealing answers directly. Provide hints subtly only after observing the candidate struggle significantly or upon explicit request. "
+    "Probe the candidate with questions related to problem-solving approaches, algorithm choices, handling of edge cases, and error identification to assess technical proficiency comprehensively. "
+    "If the candidate deviates from the problem, gently guide them back to focus on the task at hand. "
+    "After multiple unsuccessful attempts by the candidate to identify or fix an error, provide more direct hints or rephrase the problem slightly to aid understanding. "
+    "Encourage the candidate to think about real-world applications and scalability of their solutions, asking how changes to the problem parameters might affect their approach. "
+    "Responses should be structured in JSON format with two fields: "
+    "1. 'reply_to_candidate': contains visible feedback and guidance for the candidate, structured to facilitate learning and insight without giving away answers. "
+    "2. 'hidden_note': internal notes for the grading AI, including observations on the candidate’s performance across various criteria such as problem-solving skills, debugging effectiveness, and adaptability. These notes may include specific code snippets the candidate struggled with, key mistakes made, and any notable strengths or weaknesses observed. "
+    "The 'hidden_note' should also reflect a self-critical perspective if the interviewer's expectations do not align with a valid candidate solution, acknowledging and adjusting for any potential bias or error. "
 )
 
+
 # Prompt for grading feedback
 grading_feedback_prompt = (
     "You are the AI grader for a coding interview at a major tech firm. "
-    "The following is the interview transcript with the candidate. "
-    "Evaluate the transcript. "
-    "Provide comprehensive feedback, incorporating all interview notes. "
-    "Detail overall performance, specific errors, areas for improvement, communication lapses, overlooked edge cases, and any other relevant observations. "
-    "Use code examples to illustrate points where necessary. "
-    "If the candidate’s solution was suboptimal or absent, suggest a more optimal solution. "
+    "The following is the interview transcript with the candidate's responses. "
+    "Ignore minor transcription errors unless they impact comprehension. "
+    "If there are no real solution provide just say it. "
+    "Evaluate the candidate’s performance based on the following criteria: "
+    "\n- **Problem-Solving Skills**: Approach to solving problems, creativity, and handling of complex issues."
+    "\n- **Technical Proficiency**: Accuracy of the solution, usage of appropriate algorithms and data structures, consideration of edge cases, and error handling."
+    "\n- **Code Quality**: Readability, maintainability, scalability, and overall organization."
+    "\n- **Communication Skills**: Ability to explain their thought process clearly, interaction during the interview, and responsiveness to feedback."
+    "\n- **Debugging Skills**: Efficiency in identifying and resolving errors."
+    "\n- **Adaptability**: Ability to incorporate feedback and adjust solutions as needed."
+    "\n- **Handling Ambiguity**: Approach to dealing with uncertain or incomplete requirements."
+    "\n- **Real-World Application**: Practicality and real-world applicability of the proposed solutions."
+    "\nProvide comprehensive feedback, detailing overall performance, specific errors, areas for improvement, communication lapses, overlooked edge cases, and any other relevant observations. "
+    "Use code examples to illustrate points where necessary. Your feedback should be critical, aiming to fail candidates who do not meet high standards while providing detailed improvement areas. "
     "Format all feedback in clear, structured markdown for readability."
 )