Add prompt hub for various use-cases (#9879)

Use prompt hub in our use-case docs and guides.

docs/extras/use_cases/code_understanding.ipynb

@@ -921,6 +921,48 @@
     "llm(\"Question: In bash, how do I list all the text files in the current directory that have been modified in the last month? Answer:\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains.question_answering import load_qa_chain\n",
+    "\n",
+    "# Prompt\n",
+    "template = \"\"\"Use the following pieces of context to answer the question at the end. \n",
+    "If you don't know the answer, just say that you don't know, don't try to make up an answer. \n",
+    "Use three sentences maximum and keep the answer as concise as possible. \n",
+    "{context}\n",
+    "Question: {question}\n",
+    "Helpful Answer:\"\"\"\n",
+    "QA_CHAIN_PROMPT = PromptTemplate(\n",
+    "    input_variables=[\"context\", \"question\"],\n",
+    "    template=template,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also use the LangChain Prompt Hub to store and fetch prompts.\n",
+    "\n",
+    "This will work with your [LangSmith API key](https://docs.smith.langchain.com/).\n",
+    "\n",
+    "Let's try with a default RAG prompt, [here](https://smith.langchain.com/hub/rlm/rag-prompt)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import hub\n",
+    "QA_CHAIN_PROMPT = hub.pull(\"rlm/rag-prompt-default\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 29,
@@ -970,20 +1012,6 @@
     }
    ],
    "source": [
-    "from langchain.chains.question_answering import load_qa_chain\n",
-    "\n",
-    "# Prompt\n",
-    "template = \"\"\"Use the following pieces of context to answer the question at the end. \n",
-    "If you don't know the answer, just say that you don't know, don't try to make up an answer. \n",
-    "Use three sentences maximum and keep the answer as concise as possible. \n",
-    "{context}\n",
-    "Question: {question}\n",
-    "Helpful Answer:\"\"\"\n",
-    "QA_CHAIN_PROMPT = PromptTemplate(\n",
-    "    input_variables=[\"context\", \"question\"],\n",
-    "    template=template,\n",
-    ")\n",
-    "\n",
     "# Docs\n",
     "question = \"How can I initialize a ReAct agent?\"\n",
     "docs = retriever.get_relevant_documents(question)\n",