How to validate a clustering model without a ground truth?
In my opinion there are two ways:


Ask a few experts to assess the quality of the clusters based on a sample (after the clustering has been done, much easier than pre-annotating the whole data especially in the case of clustering)
If the clustering is done in the perspective of using the result in another task, the performance of this other task will reflect the quality of the clustering.


Imho any measure based on the distance between clusters or other technical measure would be a flawed evaluation, because it would depend on the quality of the representation. Such measures might provide some useful indications though, just not a proper evaluation for the task.