diff --git a/docs/docs/integrations/llms/huggingface_pipelines.ipynb b/docs/docs/integrations/llms/huggingface_pipelines.ipynb
index a377d9d008..2d74d44542 100644
--- a/docs/docs/integrations/llms/huggingface_pipelines.ipynb
+++ b/docs/docs/integrations/llms/huggingface_pipelines.ipynb
@@ -330,7 +330,7 @@
    "id": "da9a9239",
    "metadata": {},
    "source": [
-    "For more information refer to [OpenVINO LLM guide](https://docs.openvino.ai/2024/openvino-workflow/generative-ai-models-guide.html)."
+    "For more information refer to [OpenVINO LLM guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html) and [OpenVINO Local Pipelines notebook](./openvino.ipynb)."
    ]
   }
  ],
diff --git a/docs/docs/integrations/llms/openvino.ipynb b/docs/docs/integrations/llms/openvino.ipynb
index 1309d65b90..7c6f63e870 100644
--- a/docs/docs/integrations/llms/openvino.ipynb
+++ b/docs/docs/integrations/llms/openvino.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# OpenVINO Local Pipelines\n",
     "\n",
-    "[OpenVINO™](https://github.com/openvinotoolkit/openvino) is an open-source toolkit for optimizing and deploying AI inference. The OpenVINO™ Runtime can infer models on different hardware [devices](https://github.com/openvinotoolkit/openvino?tab=readme-ov-file#supported-hardware-matrix). It can help to boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks.\n",
+    "[OpenVINO™](https://github.com/openvinotoolkit/openvino) is an open-source toolkit for optimizing and deploying AI inference. OpenVINO™ Runtime can enable running the same model optimized across various hardware [devices](https://github.com/openvinotoolkit/openvino?tab=readme-ov-file#supported-hardware-matrix). Accelerate your deep learning performance across use cases like: language + LLMs, computer vision, automatic speech recognition, and more.\n",
     "\n",
     "OpenVINO models can be run locally through the `HuggingFacePipeline` [class](https://python.langchain.com/docs/integrations/llms/huggingface_pipeline). To deploy a model with OpenVINO, you can specify the `backend=\"openvino\"` parameter to trigger OpenVINO as backend inference framework."
    ]
@@ -73,7 +73,7 @@
    "id": "00104b27-0c15-4a97-b198-4512337ee211",
    "metadata": {},
    "source": [
-    "They can also be loaded by passing in an existing `optimum-intel` pipeline directly"
+    "They can also be loaded by passing in an existing [`optimum-intel`](https://huggingface.co/docs/optimum/main/en/intel/inference) pipeline directly"
    ]
   },
   {
@@ -221,7 +221,15 @@
    "id": "da9a9239",
    "metadata": {},
    "source": [
-    "For more information refer to [OpenVINO LLM guide](https://docs.openvino.ai/2024/openvino-workflow/generative-ai-models-guide.html)."
+    "For more information refer to:\n",
+    "\n",
+    "* [OpenVINO LLM guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html).\n",
+    "\n",
+    "* [OpenVINO Documentation](https://docs.openvino.ai/2024/home.html).\n",
+    "\n",
+    "* [OpenVINO Get Started Guide](https://www.intel.com/content/www/us/en/content-details/819067/openvino-get-started-guide.html).\n",
+    "  \n",
+    "* [RAG Notebook with LangChain](https://github.com/openvinotoolkit/openvino_notebooks/tree/master/notebooks/llm-chatbot)."
    ]
   }
  ],