week1 day1 exercise

1_foundations/community_contributions/kavyahj04/week01/day1.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6a773ad4",
+   "metadata": {},
+   "source": []
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+  {
+   "cell_type": "markdown",
+   "id": "ae62ae1b",
+   "metadata": {},
+   "source": [
+    "<table style=\"margin: 0; text-align: left; width:100%\">\n",
+    "    <tr>\n",
+    "        <td>\n",
+    "            <h2 style=\"color:#ff7800;\">Exercise</h2>\n",
+    "            <span style=\"color:#ff7800;\">Now try this commercial application:<br/>\n",
+    "            First ask the LLM to pick a business area that might be worth exploring for an Agentic AI opportunity.<br/>\n",
+    "            Then ask the LLM to present a pain-point in that industry - something challenging that might be ripe for an Agentic solution.<br/>\n",
+    "            Finally have 3 third LLM call propose the Agentic AI solution. <br/>\n",
+    "            We will cover this at up-coming labs, so don't worry if you're unsure.. just give it a try!\n",
+    "            </span>\n",
+    "        </td>\n",
+    "    </tr>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "309971e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "from openai import OpenAI\n",
+    "from IPython.display import display, Markdown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06b2d898",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load_dotenv(override=True)\n",
+    "api_key = os.getenv(\"OPENAI_API_KEY\")\n",
+    "\n",
+    "if api_key:\n",
+    "    print(f\"Open AI API Key found and key starts with - {api_key[0:9]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b474363",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "openai = OpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5145587",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First create the messages:\n",
+    "message = \"You are a helpful assistant who pick a business area that might be worth exploring for an Agentic AI opportunity.\"\n",
+    "messages = [{\"role\": \"user\", \"content\": \"Something here\"}]\n",
+    "\n",
+    "# Then make the first call:\n",
+    "\n",
+    "response = openai.chat.completions.create(model=\"gpt-5.4-mini\", messages=messages)\n",
+    "\n",
+    "# Then read the business idea:\n",
+    "\n",
+    "business_area = response.choices[0].message.content\n",
+    "\n",
+    "# And repeat! In the next message, include the business area within the message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf562b90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages = [{\"role\": \"user\", \"content\": f\"Here is the business area: {business_area} present a pain point in this industry.\"}]\n",
+    "\n",
+    "response = openai.chat.completions.create(model=\"gpt-5.4-mini\", messages=messages)\n",
+    "\n",
+    "pain_point = response.choices[0].message.content\n",
+    "\n",
+    "# And repeat! In the next message, include the business area within the message\n",
+    "messages = [{\"role\": \"user\", \"content\": f\"Here is the business area: {business_area} present a pain point in this industry{pain_point}, propose the Agentic AI solution.While proposing follow this structure. 1. Business Area 2. Pain Point 3. Agentic AI solution\"}]\n",
+    "\n",
+    "response = openai.chat.completions.create(model=\"gpt-5.4-mini\", messages=messages)\n",
+    "\n",
+    "agentic_solution = response.choices[0].message.content\n",
+    "\n",
+    "display(Markdown(agentic_solution))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}