diff --git a/docs/build/eps.md b/docs/build/eps.md
index bce2d1defb248..ee0041a4b8362 100644
--- a/docs/build/eps.md
+++ b/docs/build/eps.md
@@ -271,7 +271,7 @@ See more information on the OpenVINO™ Execution Provider [here](../execution-p
   *2024.3 is the current recommended OpenVINO™ version. [OpenVINO™ 2023.3](https://docs.openvino.ai/2023.3/home.html) is minimal OpenVINO™ version requirement.*
 
 2. Configure the target hardware with specific follow on instructions:
-   * To configure Intel<sup>®</sup> Processor Graphics(GPU) please follow these instructions: [Windows](https://docs.openvino.ai/latest/openvino_docs_install_guides_configurations_for_intel_gpu.html#gpu-guide-windows), [Linux](https://docs.openvino.ai/latest/openvino_docs_install_guides_configurations_for_intel_gpu.html#linux)
+   * To configure Intel<sup>®</sup> Processor Graphics(GPU) please follow these instructions: [Windows](https://docs.openvino.ai/2024/get-started/configurations/configurations-intel-gpu.html#windows), [Linux](https://docs.openvino.ai/2024/get-started/configurations/configurations-intel-gpu.html#linux)
 
 
 3. Initialize the OpenVINO™ environment by running the setupvars script as shown below. This is a required step:
@@ -306,7 +306,7 @@ See more information on the OpenVINO™ Execution Provider [here](../execution-p
 * `--use_openvino` builds the OpenVINO™ Execution Provider in ONNX Runtime.
 * `<hardware_option>`: Specifies the default hardware target for building OpenVINO™ Execution Provider. This can be overriden dynamically at runtime with another option (refer to [OpenVINO™-ExecutionProvider](../execution-providers/OpenVINO-ExecutionProvider.md#summary-of-options) for more details on dynamic device selection). Below are the options for different Intel target devices.
 
-Refer to [Intel GPU device naming convention](https://docs.openvino.ai/latest/openvino_docs_OV_UG_supported_plugins_GPU.html#device-naming-convention) for specifying the correct hardware target in cases where both integrated and discrete GPU's co-exist.
+Refer to [Intel GPU device naming convention](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html#device-naming-convention) for specifying the correct hardware target in cases where both integrated and discrete GPU's co-exist.
 
 | Hardware Option | Target Device |
 | --------------- | ------------------------|
diff --git a/docs/execution-providers/OpenVINO-ExecutionProvider.md b/docs/execution-providers/OpenVINO-ExecutionProvider.md
index fa71f70b0c277..6a64703b39abc 100644
--- a/docs/execution-providers/OpenVINO-ExecutionProvider.md
+++ b/docs/execution-providers/OpenVINO-ExecutionProvider.md
@@ -90,7 +90,7 @@ To use csharp api for openvino execution provider create a custom nuget package.
 
 ### OpenCL queue throttling for GPU devices
 
-Enables [OpenCL queue throttling](https://docs.openvino.ai/latest/groupov_runtime_ocl_gpu_prop_cpp_api.html?highlight=throttling) for GPU devices. Reduces CPU utilization when using GPUs with OpenVINO EP.
+Enables [OpenCL queue throttling](https://docs.openvino.ai/2024/api/c_cpp_api/group__ov__runtime__ocl__gpu__prop__cpp__api.html) for GPU devices. Reduces CPU utilization when using GPUs with OpenVINO EP.
 
 ### Model caching
 
@@ -118,7 +118,7 @@ Int8 models are supported on CPU, GPU and NPU.
 
 OpenVINO™ Execution Provider now  supports ONNX models that store weights in external files. It is especially useful for models larger than 2GB because of protobuf limitations.
 
-See the [OpenVINO™ ONNX Support documentation](https://docs.openvino.ai/latest/classov_1_1Core.html).
+See the [OpenVINO™ ONNX Support documentation](https://docs.openvino.ai/2024/openvino-workflow/model-preparation/convert-model-onnx.html).
 
 Converting and Saving an ONNX Model to External Data:
 Use the ONNX API's.[documentation](https://github.com/onnx/onnx/blob/master/docs/ExternalData.md#converting-and-saving-an-onnx-model-to-external-data).
@@ -177,7 +177,7 @@ Use `AUTO:<device 1><device 2>..` as the device name to delegate selection of an
 From the application point of view, this is just another device that handles all accelerators in full system.
 
 For more information on Auto-Device plugin of OpenVINO™, please refer to the
-[Intel OpenVINO™ Auto Device Plugin](https://docs.openvino.ai/latest/openvino_docs_OV_UG_supported_plugins_AUTO.html).
+[Intel OpenVINO™ Auto Device Plugin](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html#automatic-device-selection).
 
 ### Heterogeneous Execution for OpenVINO™ Execution Provider
 
@@ -186,7 +186,7 @@ The heterogeneous execution enables computing for inference on one network on se
 * To utilize accelerator's power and calculate the heaviest parts of the network on the accelerator and execute unsupported layers on fallback devices like the CPU to utilize all available hardware more efficiently during one inference.
 
 For more information on Heterogeneous plugin of OpenVINO™, please refer to the
-[Intel OpenVINO™ Heterogeneous Plugin](https://docs.openvino.ai/latest/openvino_docs_OV_UG_Hetero_execution.html).
+[Intel OpenVINO™ Heterogeneous Plugin](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/hetero-execution.html).
 
 ### Multi-Device Execution for OpenVINO EP
 
@@ -196,7 +196,7 @@ Multi-Device plugin automatically assigns inference requests to available comput
 * More consistent performance, since the devices can now share the inference burden (so that if one device is becoming too busy, another device can take more of the load)
 
 For more information on Multi-Device plugin of OpenVINO™, please refer to the
-[Intel OpenVINO™ Multi Device Plugin](https://docs.openvino.ai/latest/openvino_docs_OV_UG_Running_on_multiple_devices.html).
+[Intel OpenVINO™ Multi Device Plugin](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html#multi-stream-execution).
 
 ### Export OpenVINO Compiled Blob 
 Export the OpenVINO compiled blob as an ONNX model. Using this ONNX model for subsequent inferences avoids model recompilation and could have a positive impact on Session creation time. This feature is currently enabled for fully supported models only. It complies with the ORT session config keys
diff --git a/docs/tutorials/on-device-training/ios-app.md b/docs/tutorials/on-device-training/ios-app.md
index e61bab68596ff..91a0e9ed39f7b 100644
--- a/docs/tutorials/on-device-training/ios-app.md
+++ b/docs/tutorials/on-device-training/ios-app.md
@@ -964,5 +964,5 @@ Congratulations! You have successfully built an iOS application that can train a
 
 
 
-[Back to top](#contents)
+[Back to top](#table-of-contents)
 
diff --git a/src/routes/blogs/accelerating-phi-3/+page.svx b/src/routes/blogs/accelerating-phi-3/+page.svx
index 9a78d1103a624..d1f4f08da8f0c 100644
--- a/src/routes/blogs/accelerating-phi-3/+page.svx
+++ b/src/routes/blogs/accelerating-phi-3/+page.svx
@@ -51,7 +51,7 @@ Whether it's Windows, Linux, Android, or Mac, there's a path to infer models eff
 
 ## Try the ONNX Runtime Generate() API
 
-We are pleased to announce our new Generate() API, which makes it easier to run the Phi-3 models across a range of devices, platforms, and EP backends by wrapping several aspects of generative AI inferencing. The Generate() API makes it easy to drag and drop LLMs straight into your app. To run the early version of these models with ONNX, follow the steps [here](http://aka.ms/generate-tutorial).
+We are pleased to announce our new Generate() API, which makes it easier to run the Phi-3 models across a range of devices, platforms, and EP backends by wrapping several aspects of generative AI inferencing. The Generate() API makes it easy to drag and drop LLMs straight into your app. To run the early version of these models with ONNX, follow the steps [here](https://aka.ms/generate-tutorial).
 
 
 Example: