docs: add initial context

docs/tutorials/workflow_step_back_llamaindex_argilla.ipynb

@@ -11,9 +11,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This tutorial will show how to use step-back prompting with LlamaIndex workflows for RAG. \n",
+    "This tutorial will show how to use step-back prompting with LlamaIndex workflows for RAG integrated with Argilla.\n",
     "\n",
-    "> This approach is based on the \"[Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](https://arxiv.org/abs/2310.06117)\" paper.\n",
+    "This prompting approach is based on \"[Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](https://arxiv.org/abs/2310.06117)\". This paper suggests that the response can be improved by asking the model to take a step back and reason about the context in a more abstract way. This way, the original query is abstracted and used to retrieved the relevant information. Then, this context along with the original context and query are used to generate the final response. \n",
+    "\n",
+    "[Argilla](https://github.com/argilla-io/argilla) is a collaboration tool for AI engineers and domain experts to build high-quality datasets. By doing this, you can analyze and enhance the quality of your data, leading to improved model performance by incorporating human feedback into the loop. The integration will automatically log the query, response, retrieved contexts with their scores, and the full trace (including spans and events), along with relevant metadata in Argilla. By default, you'll have the ability to rate responses, provide feedback, and evaluate the retrieved contexts, ensuring accuracy and preventing any discrepancies.\n",
     "\n",
     "It includes the following steps:\n",
     "\n",
@@ -355,7 +357,7 @@
     "]\n",
     "\n",
     "documents = SimpleDirectoryReader(\n",
-    "    \"/Users/sdiazlor/Documents/argilla-llama-index/data\"\n",
+    "    \"../../data\"\n",
     ").load_data()\n",
     "index = VectorStoreIndex.from_documents(\n",
     "    documents=documents,\n",