diff --git a/1.chunkedgraph.md b/1.chunkedgraph.md
index 387e587..426e095 100644
--- a/1.chunkedgraph.md
+++ b/1.chunkedgraph.md
@@ -64,6 +64,8 @@ and link the image and tag in `values.yaml` (refer to `example_values.yaml`).
 
 > NOTE: Depending on your dataset, you will need to figure out the optimal limits for cpu and memory in your worker deployments. To do that adjust the `count` and `machine` variables in terraform.tfvars. It can vary with chunk size, size of supervoxels (atomic semgents in layer 1), number of edges per chunk and so on.
 
+> NOTE: The bucket storing the edges, components etc must be public. The bucket is also linked in `example_values.yaml` [here](https://github.com/seung-lab/CAVEpipelines/blob/4d0ee8d98fb8891074d6e1115ed9394e6a021cd5/helm/pychunkedgraph/example_values.yaml#L29).
+
 #### Chart Installation
 When all variables are ready, rename your values file to `values.yaml` (ignored by git because it can contain sensitive information). Then run:
 
@@ -83,7 +85,7 @@ Pods should now be in `Running` status, provided there were no issues. Run the f
 ```shell
 $ kubectl exec -ti deploy/master -- bash
 // now you're in the container
-> ingest graph <unique_test_bigtable_name> datasets/test.yml --test
+> ingest graph <unique_test_bigtable_name> datasets/test.yml --test // avoid dashes in <unique_test_bigtable_name>, neuroglancer can't handle them
 ```
 
 [RQ](https://python-rq.org/docs/) is used to create jobs. This library uses `redis` as a task queue.