Created using Colab

notebooks/Prompting_101.ipynb

@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyMfsqkpoj0pK1Cxdu7nfSuh",
+      "authorship_tag": "ABX9TyOjg7OkeSratqFL3N0gFoY1",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -43,17 +43,17 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "87a0ca71-0dc5-4b69-d644-b67638ea856a"
+        "outputId": "6bc470f0-2efe-4cd8-d3e3-1b20593ad968"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m320.6/320.6 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m320.6/320.6 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[?25h"
           ]
         }
@@ -68,12 +68,12 @@
         "import os\n",
         "\n",
         "# Set the \"OPENAI_API_KEY\" in the Python environment. Will be used by OpenAI client later.\n",
-        "os.environ[\"OPENAI_API_KEY\"] = \"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\""
+        "os.environ[\"OPENAI_API_KEY\"] = \"[OPENAI_API_KEY]\""
       ],
       "metadata": {
         "id": "xxK7EAAvr2aT"
       },
-      "execution_count": 3,
+      "execution_count": 2,
       "outputs": []
     },
     {
@@ -97,7 +97,7 @@
       "metadata": {
         "id": "La8hdWqJkFkh"
       },
-      "execution_count": 4,
+      "execution_count": 3,
       "outputs": []
     },
     {
@@ -542,7 +542,7 @@
       "metadata": {
         "id": "MghL9RV5HngY"
       },
-      "execution_count": 5,
+      "execution_count": 4,
       "outputs": []
     },
     {
@@ -555,9 +555,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "xVMysd9fexdf",
-        "outputId": "88a7953f-a58f-4513-e7ab-b07933024896"
+        "outputId": "3544324c-9f3b-4ee0-f76f-98ef1b888947"
       },
-      "execution_count": 6,
+      "execution_count": 5,
       "outputs": [
         {
           "output_type": "stream",
@@ -583,7 +583,7 @@
       "metadata": {
         "id": "80zGzWQVez9d"
       },
-      "execution_count": 7,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -598,7 +598,7 @@
         "id": "DqWLGQNke4zm",
         "outputId": "121392d7-7638-4cfe-91ae-8b456dea7d4f"
       },
-      "execution_count": 8,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -608,6 +608,62 @@
           ]
         }
       ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "response = client.chat.completions.create(\n",
+        "        model='gpt-4o',\n",
+        "        temperature=0.0,\n",
+        "        messages=[\n",
+        "            {\"role\": \"system\", \"content\": system_prompt},\n",
+        "            {\"role\": \"user\", \"content\": \"Let's play a game. Imagine the mointains are the same as AI libraries, what is the tallest montain in terms of library and the actual montain?\"}\n",
+        "        ]\n",
+        "    )"
+      ],
+      "metadata": {
+        "id": "-xCC_7fQ9Q0v"
+      },
+      "execution_count": 32,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print( response.choices[0].message.content )"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RwejpWBu9YfW",
+        "outputId": "227515bd-3c9b-409c-d8d9-373b80a251dc"
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "In the context of AI libraries, the \"tallest mountain\" could be considered the most prominent or widely used library. TensorFlow, developed by Google, is often regarded as one of the most significant and widely adopted AI libraries due to its extensive features, community support, and versatility in both research and production environments.\n",
+            "\n",
+            "In terms of actual mountains, Mount Everest is the tallest mountain above sea level, standing at 8,848 meters (29,029 feet).\n",
+            "\n",
+            "So, in this analogy:\n",
+            "- The \"tallest mountain\" in AI libraries could be TensorFlow.\n",
+            "- The tallest actual mountain is Mount Everest.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "gF2RyUc69bSU"
+      },
+      "execution_count": null,
+      "outputs": []
     }
   ]
 }