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{
"cells": [
{
"cell_type": "raw",
"metadata": {
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"source": [
"# Lab 2 Exercise - Extending the Patterns\n",
"\n",
"This notebook extends the original lab by adding the Chain of Thought pattern to enhance the evaluation process.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Import required packages\n",
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"from anthropic import Anthropic\n",
"from IPython.display import Markdown, display\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables\n",
"load_dotenv(override=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Initialize API clients\n",
"openai = OpenAI()\n",
"claude = Anthropic()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Original question generation\n",
"request = \"Please come up with a challenging, nuanced question that I can ask a number of LLMs to evaluate their intelligence. \"\n",
"request += \"Answer only with the question, no explanation.\"\n",
"messages = [{\"role\": \"user\", \"content\": request}]\n",
"\n",
"response = openai.chat.completions.create(\n",
" model=\"gpt-4o-mini\",\n",
" messages=messages,\n",
")\n",
"question = response.choices[0].message.content\n",
"print(question)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Get responses from multiple models\n",
"competitors = []\n",
"answers = []\n",
"messages = [{\"role\": \"user\", \"content\": question}]\n",
"\n",
"# OpenAI\n",
"response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n",
"answer = response.choices[0].message.content\n",
"competitors.append(\"gpt-4o-mini\")\n",
"answers.append(answer)\n",
"display(Markdown(answer))\n",
"\n",
"# Claude\n",
"response = claude.messages.create(model=\"claude-3-7-sonnet-latest\", messages=messages, max_tokens=1000)\n",
"answer = response.content[0].text\n",
"competitors.append(\"claude-3-7-sonnet-latest\")\n",
"answers.append(answer)\n",
"display(Markdown(answer))\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# NEW: Chain of Thought Evaluation\n",
"# First, let's create a detailed evaluation prompt that encourages step-by-step reasoning\n",
"\n",
"evaluation_prompt = f\"\"\"You are an expert evaluator of AI responses. Your task is to analyze and rank the following responses to this question:\n",
"\n",
"{question}\n",
"\n",
"Please follow these steps in your evaluation:\n",
"\n",
"1. For each response:\n",
" - Identify the main arguments presented\n",
" - Evaluate the clarity and coherence of the reasoning\n",
" - Assess the depth and breadth of the analysis\n",
" - Note any unique insights or perspectives\n",
"\n",
"2. Compare the responses:\n",
" - How do they differ in their approach?\n",
" - Which response demonstrates the most sophisticated understanding?\n",
" - Which response provides the most practical and actionable insights?\n",
"\n",
"3. Provide your final ranking with detailed justification for each position.\n",
"\n",
"Here are the responses:\n",
"\n",
"{'\\\\n\\\\n'.join([f'Response {i+1} ({competitors[i]}):\\\\n{answer}' for i, answer in enumerate(answers)])}\n",
"\n",
"Please provide your evaluation in JSON format with the following structure:\n",
"{{\n",
" \"detailed_analysis\": [\n",
" {{\"competitor\": \"name\", \"strengths\": [], \"weaknesses\": [], \"unique_aspects\": []}},\n",
" ...\n",
" ],\n",
" \"comparative_analysis\": \"detailed comparison of responses\",\n",
" \"final_ranking\": [\"ranked competitor numbers\"],\n",
" \"justification\": \"detailed explanation of the ranking\"\n",
"}}\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Get the detailed evaluation\n",
"evaluation_messages = [{\"role\": \"user\", \"content\": evaluation_prompt}]\n",
"\n",
"response = openai.chat.completions.create(\n",
" model=\"gpt-4o-mini\",\n",
" messages=evaluation_messages,\n",
")\n",
"detailed_evaluation = response.choices[0].message.content\n",
"print(detailed_evaluation)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Parse and display the results in a more readable format\n",
"\n",
"# Clean up the JSON string by removing markdown code block markers\n",
"json_str = detailed_evaluation.replace(\"```json\", \"\").replace(\"```\", \"\").strip()\n",
"\n",
"evaluation_dict = json.loads(json_str)\n",
"\n",
"print(\"Detailed Analysis:\")\n",
"for analysis in evaluation_dict[\"detailed_analysis\"]:\n",
" print(f\"\\nCompetitor: {analysis['competitor']}\")\n",
" print(\"Strengths:\")\n",
" for strength in analysis['strengths']:\n",
" print(f\"- {strength}\")\n",
" print(\"\\nWeaknesses:\")\n",
" for weakness in analysis['weaknesses']:\n",
" print(f\"- {weakness}\")\n",
" print(\"\\nUnique Aspects:\")\n",
" for aspect in analysis['unique_aspects']:\n",
" print(f\"- {aspect}\")\n",
"\n",
"print(\"\\nComparative Analysis:\")\n",
"print(evaluation_dict[\"comparative_analysis\"])\n",
"\n",
"print(\"\\nFinal Ranking:\")\n",
"for i, rank in enumerate(evaluation_dict[\"final_ranking\"]):\n",
" print(f\"{i+1}. {competitors[int(rank)-1]}\")\n",
"\n",
"print(\"\\nJustification:\")\n",
"print(evaluation_dict[\"justification\"])\n"
]
},
{
"cell_type": "raw",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"## Pattern Analysis\n",
"\n",
"This enhanced version uses several agentic design patterns:\n",
"\n",
"1. **Multi-agent Collaboration**: Sending the same question to multiple LLMs\n",
"2. **Evaluation/Judgment Pattern**: Using one LLM to evaluate responses from others\n",
"3. **Parallel Processing**: Running multiple models simultaneously\n",
"4. **Chain of Thought**: Added a structured, step-by-step evaluation process that breaks down the analysis into clear stages\n",
"\n",
"The Chain of Thought pattern is particularly valuable here because it:\n",
"- Forces the evaluator to consider multiple aspects of each response\n",
"- Provides more detailed and structured feedback\n",
"- Makes the evaluation process more transparent and explainable\n",
"- Helps identify specific strengths and weaknesses in each response\n"
]
}
],
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