[87780] Fix link to POT in docs (#136)

Author: tsavina

docs/Workbench_DG/Export_Project.md

@@ -6,7 +6,7 @@ You can download an archive with artifacts of your project:
 * Dataset
 * Accuracy measurement configuration delivered as a valid configuration file for the 
   [Accuracy Checker](https://docs.openvino.ai/latest/omz_tools_accuracy_checker.html)
-* [Post-training Optimization Tool](https://docs.openvino.ai/latest/pot_README.html) calibration configuration file
+* [Post-training Optimization Tool](@ref pot_introduction) calibration configuration file
 
 A downloaded archive also contains information on the project and best model performance.
 

docs/Workbench_DG/Overview.md

@@ -74,7 +74,7 @@ Component  |                 Description
 | [Model Optimizer](https://docs.openvinotoolkit.org/latest/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) |Optimize and transform models trained in supported frameworks to the IR format. <br>Supported frameworks include TensorFlow\*, Caffe\*, Kaldi\*, MXNet\*, and ONNX\* format.  
 | [Benchmark Tool](https://docs.openvinotoolkit.org/latest/openvino_inference_engine_tools_benchmark_tool_README.html)| Estimate deep learning model inference performance on supported devices.   
 | [Accuracy Checker](https://docs.openvinotoolkit.org/latest/omz_tools_accuracy_checker.html)| Evaluate the accuracy of a model by collecting one or several metric values. 
-| [Post-Training Optimization Tool](https://docs.openvinotoolkit.org/latest/pot_README.html)| Optimize pretrained models with lowering the precision of a model from floating-point precision(FP32 or FP16) to integer precision (INT8), without the need to retrain or fine-tune models.                               |
+| [Post-Training Optimization Tool](@ref pot_introduction)| Optimize pretrained models with lowering the precision of a model from floating-point precision(FP32 or FP16) to integer precision (INT8), without the need to retrain or fine-tune models.                               |
 
 
 @sphinxdirective