Fix nit

template/{{.input_root_dir}}/docs/ml-pull-request.md.tmpl

@@ -23,11 +23,11 @@ of the Git repo. Support for running tests against pull requests from repo forks
 is planned for the future.
 
 ## Viewing test status and debug logs
-Opening a pull request will trigger a  
+Opening a pull request will trigger the project-specific (generated by the deploy CI/CD workflow)
 {{- if or (eq .input_cicd_platform `github_actions`) (eq .input_cicd_platform `github_actions_for_github_enterprise_servers`) -}} 
-[workflow](../.github/workflows/{{ .input_project_name }}-run-tests.yml)
+[run-tests.yml workflow](../.github/workflows/)
 {{- else if (eq .input_cicd_platform `azure_devops`) -}}
-[Azure DevOps Pipeline](../.azure/devops-pipelines/{{ .input_project_name }}-tests-ci.yml)
+[tests-ci.yml Azure DevOps Pipeline](../.azure/devops-pipelines/)
 {{ end }} 
 that runs unit and integration tests for the model training (and feature engineering if added) pipeline on Databricks against a test dataset.
 You can view test status and debug logs from the pull request UI, and push new commits to your pull request branch
@@ -74,14 +74,16 @@ After merging your pull request, subsequent runs of the model training and batch
 jobs in staging and production will automatically use your updated ML code.
 {{- end }}
 
+{{- if (eq .input_setup_cicd_and_project `CICD_and_Project`)}}
 You can track the state of the ML pipelines for the current project from the MLflow registered model UI. Links:
 {{ if (eq .input_include_models_in_unity_catalog `no`) }}
 * [Staging workspace registered model]({{template `databricks_staging_workspace_host` .}}/ml/models/staging-{{template `model_name` .}})
 * [Prod workspace registered model]({{template `databricks_prod_workspace_host` .}}/ml/models/prod-{{template `model_name` .}})
 {{- else -}} 
 * [Staging model in UC]({{template `databricks_staging_workspace_host` .}}/explore/data/models/staging/{{ .input_project_name }}/{{template `model_name` .}})
 * [Prod model in UC]({{template `databricks_prod_workspace_host` .}}/explore/data/models/prod/{{ .input_project_name }}/{{template `model_name` .}})
-{{end}}. 
+{{end}} 
+{{- end}}
 
 In both the staging and prod workspaces, the MLflow registered model contains links to:
 * The model versions produced through automated retraining