diff --git a/training/README.md b/training/README.md
index 22debd158..c1f6a313f 100644
--- a/training/README.md
+++ b/training/README.md
@@ -34,15 +34,15 @@ In order to run accelerated AI workloads, we've prepared [bootc](https://github.
 
 Note: AI content is huge and requires a lot of disk space >200GB free to build.
 
-# How to build Instructlab containers
+# How to build InstructLab containers
 
 In order to do AI Training you need to build instructlab container images.
 
-Simply execute `make instructlab-<platform>`. For example:
+Simply execute `make instruct-<platform>`. For example:
 
-* make instructlab-amd
-* make instructlab-intel
-* make instructlab-nvidia
+* make instruct-amd
+* make instruct-intel
+* make instruct-nvidia
 
 Once you have these container images built it is time to build vllm.
 
@@ -84,11 +84,14 @@ make nvidia REGISTRY=myregistry.com REGISTRY_ORG=ai-training IMAGE_NAME=nvidia I
 
 # How to build Cloud ready images
 
-Bootc container images can be installed on physical machines, virtual machines and in the cloud.  Often it is useful to add the cloud-init package when running the operarting systems in the cloud.
+Bootc container images can be installed on physical machines, virtual machines and in the cloud.  Often it is useful to add the cloud-init package when running the operating systems in the cloud.
+
+To add cloud-init to your existing bootc container image, executing `make cloud-<platform>` should be enough. For example to build the `cloud-nvidia`, `cloud-amd` and `cloud-intel` bootc containers, respectively:
 
-To add cloud-init to your existing bootc container image, executing make cloud-<platform>
 ```
-make nvidia REGISTRY=myregistry.com REGISTRY_ORG=ai-training IMAGE_NAME=nvidia IMAGE_TAG=v1  should be enough. For example to build the `cloud-nvidia`, `cloud-amd` and `cloud-intel` bootc containers, respectively:
+make cloud-nvidia
+make cloud-amd
+make cloud-intel
 ```
 
 # Troubleshooting