diff --git a/docs/modules/chains/getting_started.ipynb b/docs/modules/chains/getting_started.ipynb
index bbb35a7f..00118a9f 100644
--- a/docs/modules/chains/getting_started.ipynb
+++ b/docs/modules/chains/getting_started.ipynb
@@ -26,12 +26,13 @@
     "\n",
     "The `LLMChain` is a simple chain that takes in a prompt template, formats it with the user input and returns the response from an LLM.\n",
     "\n",
+    "\n",
     "To use the `LLMChain`, first create a prompt template."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "tags": []
    },
@@ -56,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "tags": []
    },
@@ -67,7 +68,7 @@
      "text": [
       "\n",
       "\n",
-      "Cheerful Toes.\n"
+      "SockSplash!\n"
      ]
     }
    ],
@@ -88,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "tags": []
    },
@@ -97,7 +98,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Rainbow Footwear Co.\n"
+      "Rainbow Sox Co.\n"
      ]
     }
    ],
@@ -130,17 +131,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'adjective': 'lame',\n",
+       "{'adjective': 'corny',\n",
        " 'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -153,7 +154,7 @@
     "    prompt=PromptTemplate.from_template(prompt_template)\n",
     ")\n",
     "\n",
-    "llm_chain(inputs={\"adjective\":\"lame\"})"
+    "llm_chain(inputs={\"adjective\":\"corny\"})"
    ]
   },
   {
@@ -165,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -174,25 +175,47 @@
        "{'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "llm_chain(\"lame\", return_only_outputs=True)"
+    "llm_chain(\"corny\", return_only_outputs=True)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If the `Chain` only takes one input key (i.e. only has one element in its `input_variables`), you can  use `run` method. Note that `run` outputs a string instead of a dictionary."
+    "If the `Chain` only outputs one output key (i.e. only has one element in its `output_keys`), you can  use `run` method. Note that `run` outputs a string instead of a dictionary."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['text']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# llm_chain only has one output key, so we can use run\n",
+    "llm_chain.output_keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -201,47 +224,47 @@
        "'Why did the tomato turn red? Because it saw the salad dressing!'"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "llm_chain.run({\"adjective\":\"lame\"})"
+    "llm_chain.run({\"adjective\":\"corny\"})"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Besides, in the case of one input key, you can input the string directly without specifying the input mapping."
+    "In the case of one input key, you can input the string directly without specifying the input mapping."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'adjective': 'lame',\n",
+       "{'adjective': 'corny',\n",
        " 'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# These two are equivalent\n",
-    "llm_chain.run({\"adjective\":\"lame\"})\n",
-    "llm_chain.run(\"lame\")\n",
+    "llm_chain.run({\"adjective\":\"corny\"})\n",
+    "llm_chain.run(\"corny\")\n",
     "\n",
     "# These two are also equivalent\n",
-    "llm_chain(\"lame\")\n",
-    "llm_chain({\"adjective\":\"lame\"})"
+    "llm_chain(\"corny\")\n",
+    "llm_chain({\"adjective\":\"corny\"})"
    ]
   },
   {
@@ -262,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -271,7 +294,7 @@
        "'The next four colors of a rainbow are green, blue, indigo, and violet.'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -309,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -336,7 +359,7 @@
        "'ChatGPT is an AI language model developed by OpenAI. It is based on the GPT-3 architecture and is capable of generating human-like responses to text prompts. ChatGPT has been trained on a massive amount of text data and can understand and respond to a wide range of topics. It is often used for chatbots, virtual assistants, and other conversational AI applications.'"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -365,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -385,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -398,12 +421,12 @@
       "\u001b[36;1m\u001b[1;3mRainbow Socks Co.\u001b[0m\n",
       "\u001b[33;1m\u001b[1;3m\n",
       "\n",
-      "\"Step into Color with Rainbow Socks Co!\"\u001b[0m\n",
+      "\"Step into Color with Rainbow Socks!\"\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
       "\n",
       "\n",
-      "\"Step into Color with Rainbow Socks Co!\"\n"
+      "\"Step into Color with Rainbow Socks!\"\n"
      ]
     }
    ],
@@ -434,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -468,12 +491,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, we can try running the chain that we called."
+    "Now, we can try running the chain that we called.\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -483,7 +507,7 @@
       "Concatenated output:\n",
       "\n",
       "\n",
-      "Kaleidoscope Socks.\n",
+      "Socktastic Colors.\n",
       "\n",
       "\"Put Some Color in Your Step!\"\n"
      ]
@@ -531,7 +555,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.8.16"
   },
   "vscode": {
    "interpreter": {