diff --git a/docs/source/tutorials/basic_transit_identification_with_prebuilt_components.md b/docs/source/tutorials/basic_transit_identification_with_prebuilt_components.md
index 9ebf847..dbee8cc 100644
--- a/docs/source/tutorials/basic_transit_identification_with_prebuilt_components.md
+++ b/docs/source/tutorials/basic_transit_identification_with_prebuilt_components.md
@@ -54,16 +54,16 @@ where, in this case, the temporary dips are transiting events.
 `qusi` uses Weights & Biases (`wandb`), a machine learning logging platform, to record metrics from training experiments. Among other things, it will create plots showing the training progress and allow easy comparison among the various runs. While you can run the `wandb` platform locally, it's easiest to use their cloud platform, which has [free academic research team projects and free personal projects](https://wandb.ai/site/pricing). To use it with `qusi`, [sign up for an account](https://wandb.ai/site), then from your project directory use
 
 ```sh
-wandb login
+(cd sessions && wandb login)
 ```
 
 to login. If you want to proceed without a `wandb` account and log the data offline, you will need to run
 
 ```sh
-wandb offline
+(cd sessions && wandb offline)
 ```
 
-This will only log runs locally. If you choose the offline route, at some point, you will want to follow [their guide to run the local server](https://docs.wandb.ai/guides/hosting/how-to-guides/basic-setup) so that you can view the metric plots. However, for the moment, just running `wandb offline` will allow you to proceed with this tutorial.
+This will only log runs locally. If you choose the offline route, at some point, you will want to follow [their guide to run the local server](https://docs.wandb.ai/guides/hosting/how-to-guides/basic-setup) so that you can view the metric plots. However, for the moment, just running `wandb offline` (from the sessions directory) will allow you to proceed with this tutorial.
 
 ## Train the network