diff --git a/docs/genai/tutorials/finetune.md b/docs/genai/tutorials/finetune.md
index 5d0302b896dfc..3dd739d80340b 100644
--- a/docs/genai/tutorials/finetune.md
+++ b/docs/genai/tutorials/finetune.md
@@ -65,7 +65,7 @@ Olive generates models and adapters in ONNX format. These models and adapters ca
 
    Note: this operations requires a system with an NVIDIA GPU, with CUDA installed
 
-   Use the `olive fine-tune` command: https://microsoft.github.io/Olive/features/cli.html#finetune
+   Use the `olive fine-tune` command: https://microsoft.github.io/Olive/how-to/cli/cli-finetune.html
 
    Here is an example usage of the command:
 
@@ -75,12 +75,12 @@ Olive generates models and adapters in ONNX format. These models and adapters ca
 
 2. Optionally, quantize your model
 
-   Use the `olive quantize` command: https://microsoft.github.io/Olive/features/cli.html#quantize
+   Use the `olive quantize` command: https://microsoft.github.io/Olive/how-to/cli/cli-quantize.html
 
 
 3. Generate the ONNX model and adapter using the quantized model
 
-   Use the `olive auto-opt` command for this step: https://microsoft.github.io/Olive/features/cli.html#auto-opt
+   Use the `olive auto-opt` command for this step: https://microsoft.github.io/Olive/how-to/cli/cli-auto-opt.html
 
    The `--adapter path` can either be a HuggingFace adapter reference, or a path to the adapter you fine-tuned above.
 
@@ -162,4 +162,4 @@ python app.py -m <model folder> -a <.onnx_adapter files> -t <prompt template> -s
 ## References
 
 * [Python API docs](../api/python.md#adapter-class)
-* [Olive CLI docs](https://microsoft.github.io/Olive/features/cli.html)
+* [Olive CLI docs](https://microsoft.github.io/Olive/how-to/index.html#working-with-the-cli)
diff --git a/docs/tutorials/OpenVINO_EP_samples/squeezenet_classification_cpp.md b/docs/tutorials/OpenVINO_EP_samples/squeezenet_classification_cpp.md
index d0759ac028d55..46d32902393b7 100644
--- a/docs/tutorials/OpenVINO_EP_samples/squeezenet_classification_cpp.md
+++ b/docs/tutorials/OpenVINO_EP_samples/squeezenet_classification_cpp.md
@@ -14,7 +14,7 @@ The source code for this sample is available [here](https://github.com/microsoft
 # How to build
 
 ## Prerequisites
-1. [The Intel<sup>®</sup> Distribution of OpenVINO toolkit](https://docs.openvinotoolkit.org/latest/index.html)
+1. [The Intel<sup>®</sup> Distribution of OpenVINO toolkit](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html)
 
 2. Use opencv (use the same opencv package that comes builtin with Intel<sup>®</sup> Distribution of OpenVINO toolkit)
 
diff --git a/docs/tutorials/OpenVINO_EP_samples/tiny_yolo_v2_object_detection_python.md b/docs/tutorials/OpenVINO_EP_samples/tiny_yolo_v2_object_detection_python.md
index e6c6e756a2087..3ee8c610ef9d6 100644
--- a/docs/tutorials/OpenVINO_EP_samples/tiny_yolo_v2_object_detection_python.md
+++ b/docs/tutorials/OpenVINO_EP_samples/tiny_yolo_v2_object_detection_python.md
@@ -14,7 +14,7 @@ The source code for this sample is available [here](https://github.com/microsoft
 # How to build
 
 ## Prerequisites
-1. [The Intel<sup>®</sup> Distribution of OpenVINO toolkit](https://docs.openvinotoolkit.org/latest/index.html)
+1. [The Intel<sup>®</sup> Distribution of OpenVINO toolkit](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html)
 
 2. Download the latest tinyYOLOv2 model from the ONNX Model Zoo.
    This model was adapted from [ONNX Model Zoo](https://github.com/onnx/models).Download the latest version of the [tinyYOLOv2](https://github.com/onnx/models/tree/main/validated/vision/object_detection_segmentation/tiny-yolov2) model from here.
diff --git a/docs/tutorials/csharp/yolov3_object_detection_csharp.md b/docs/tutorials/csharp/yolov3_object_detection_csharp.md
index dce5c44694eea..56f00b2a758eb 100644
--- a/docs/tutorials/csharp/yolov3_object_detection_csharp.md
+++ b/docs/tutorials/csharp/yolov3_object_detection_csharp.md
@@ -23,7 +23,7 @@ The source code for this sample is available [here](https://github.com/microsoft
 ## Prerequisites
 1. Install [.NET Core 3.1](https://dotnet.microsoft.com/download/dotnet-core/3.1) or higher for you OS (Mac, Windows or Linux).
 
-2. [The Intel<sup>®</sup> Distribution of OpenVINO toolkit](https://docs.openvinotoolkit.org/latest/index.html)
+2. [The Intel<sup>®</sup> Distribution of OpenVINO toolkit](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html)
 
 3. Use any sample Image as input to the sample.