diff --git a/docs/IE_PLUGIN_DG/plugin_transformation_pipeline/low_precision_transformations/lpt.md b/docs/IE_PLUGIN_DG/plugin_transformation_pipeline/low_precision_transformations/lpt.md
index 0267e801004..a269bd85682 100644
--- a/docs/IE_PLUGIN_DG/plugin_transformation_pipeline/low_precision_transformations/lpt.md
+++ b/docs/IE_PLUGIN_DG/plugin_transformation_pipeline/low_precision_transformations/lpt.md
@@ -285,7 +285,7 @@ available from the Inference Engine API. For example, the part of performance co
 As result all operations (except not quantized `SoftMax` at the end of the model) in OpenVINO™ CPU plugin are inferred in low precision. Note, please, in the result model there are `FakeQuantize` operations in FP32 but the plugin responsibility is fuse these operations with previous operations. OpenVINO™ CPU plugin achieves maximum optimized inference for all operations by fusing INT8 `Convolution` with FP32 output with `FakeQuantize` operation with FP32 input and INT8 output. In this case OpenVINO™ CPU plugin uses INT8 and FP32 vectorized instructions but reports about one INT8 kernel usage for inference, which is the most optimized for this case.
 
 ## Mixed precision
-If LPT input model operation output has `fp16` precision then dequantization computations still occurs in `fp32` precision. This approach is used to avoid accuracy loss in `fp16` arithmetic computations. Note, the latest dequantization operation output has `fp16` precision.
+If LPT input model operation output has `fp16` precision then dequantization computations still occurs in `fp32` precision. This approach is used to avoid accuracy loss in `fp16` arithmetic computations. The ultimate output of the dequantization operation  will have the `fp16` precision, as expected.
 
 ## Customization
 Low Precision Transformations can be customizable. Build-in customization options: