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--- a/examples/storm/storm.ipynb
+++ b/examples/storm/storm.ipynb
@@ -6,25 +6,35 @@
    "source": [
     "# STORM\n",
     "\n",
-    "[STORM](https://arxiv.org/abs/2402.14207) is a research assistant by Shao, et. al that extends the idea of \"outline-driven RAG\" for richer article generation.\n",
+    "[STORM](https://arxiv.org/abs/2402.14207) is a research assistant designed by Shao, et. al that extends the idea of \"outline-driven RAG\" for richer article generation.\n",
     "\n",
-    "It is tasked with generating Wikipedia-like ariticles on a user-provided topic.  It has  a few main stages:\n",
+    "STORM is designed to generate Wikipedia-style ariticles on a user-provided topic. It applies two main insights to produce more organized and comprehensive articles:\n",
+    "\n",
+    "1. Creating an outline (planning) by querying similar topics helps improve coverage.\n",
+    "2. Multi-perspective, grounded (in search) conversation simulation helps increase the reference count and information density. \n",
+    "\n",
+    "The control flow looks like the diagram below.\n",
+    "\n",
+    "![STORM diagram](./img/storm.png)\n",
+    "\n",
+    "STORM has a few main stages:\n",
     "\n",
     "1. Generate initial outline + Survey related subjects\n",
     "2. Identify distinct perspectives\n",
     "3. \"Interview subject matter experts\" (role-playing LLMs)\n",
-    "4. Refine outline\n",
-    "5. Write article\n",
+    "4. Refine outline (using references)\n",
+    "5. Write sections, then write article\n",
     "\n",
     "\n",
-    "The expert interviews stage ocurrs between the article writer and each role-playing agent and itself is a loop, where the \"expert\" is able to query external knowledge and respond to pointed questions.\n",
+    "The expert interviews stage ocurrs between the role-playing article writer and a research expert. The \"expert\" is able to query external knowledge and respond to pointed questions, saving cited sources to a vectorstore so that the later refinement stages can synthesize the full article.\n",
     "\n",
-    "Couple hyperparameters to restrict the infinite research breadth:\n",
+    "There are a couple hyperparameters you can set to restrict the (potentially) infinite research breadth:\n",
     "\n",
-    "N: Number of perspectives to survey / use (2->3)\n",
-    "M: Max number of conversation turns in step (3)\n",
+    "N: Number of perspectives to survey / use (Steps 2->3)\n",
+    "M: Max number of conversation turns in step (Step 3)\n",
     "\n",
-    "The paper uses DSPY and few-shot examples to adapt but we'll just use functioncalling here."
+    "\n",
+    "## Prerequisites"
    ]
   },
   {
@@ -33,7 +43,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %pip install langchain_community langchain_openai langchain_fireworks langgraph wikipedia tavily-python scikit-learn duckduckgo"
+    "# %pip install -U langchain_community langchain_openai langgraph wikipedia  scikit-learn  langchain_fireworks\n",
+    "# We use one or the other search engine below\n",
+    "# %pip install -U duckduckgo tavily-python"
    ]
   },
   {
@@ -48,13 +60,34 @@
     "# !CFLAGS=\"-I $(brew --prefix graphviz)/include\" LDFLAGS=\"-L $(brew --prefix graphviz)/lib\" pip install -U pygraphviz"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if os.environ.get(var):\n",
+    "        return\n",
+    "    os.environ[var] = getpass.getpass(var + \":\")\n",
+    "\n",
+    "# Set for tracing\n",
+    "os.environ[\"LANGCHAIN_TRACING_V2\"[ = \"true\"\n",
+    "os.environ[\"LANGCHAIN_PROJECT\"] = \"STORM\"\n",
+    "_set_env(\"LANGCHAIN_API_KEY\") \n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Select LLMs\n",
     "\n",
-    "We will have a faster LLM do most of the work, but a slower, long-context model distill the conversations and write the final report."
+    "We will have a faster LLM do most of the work, but a slower, long-context model to distill the conversations and write the final report."
    ]
   },
   {