diff --git a/README.Rmd b/README.Rmd
index 97d8fed6..42b6cb70 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -185,8 +185,9 @@ ggeffects::ggpredict(fit, "depth [50:400, by=2]") |> plot()
 
 If the depth effect was parametric and not a penalized smoother, we could have alternatively used `ggeffects::ggeffect()` for a fast marginal effect plot.
 
-Predict on new data:
-(NOTE: we will use the qce_grid object inlcuded in the package which contains all the points (a raster equivalent) on which the model output will predicted new values)
+Next, we can predict on new data.
+We will use a data frame `qcs_grid` from the package, which contains all the locations (and covariates) at which we wish to predict.
+Here, these `newdata` are a grid, or raster, covering our survey.
 
 ```{r}
 p <- predict(fit, newdata = qcs_grid)
diff --git a/README.md b/README.md
index d9a12634..1ee6dd43 100644
--- a/README.md
+++ b/README.md
@@ -81,8 +81,8 @@ X <- matrix(rnorm(m*k), nrow=m); Y <- matrix(rnorm(n*k), ncol=n)
 system.time(X %*% Y)
 ```
 
-The result ('elapsed') should take a fraction of a second (e.g., 0.03 s), not
-multiple seconds.
+The result (‘elapsed’) should take a fraction of a second (e.g., 0.03
+s), not multiple seconds.
 
 ## Overview
 
@@ -339,7 +339,10 @@ If the depth effect was parametric and not a penalized smoother, we
 could have alternatively used `ggeffects::ggeffect()` for a fast
 marginal effect plot.
 
-Predict on new data:
+Next, we can predict on new data. We will use a data frame `qcs_grid`
+from the package, which contains all the locations (and covariates) at
+which we wish to predict. Here, these `newdata` are a grid, or raster,
+covering our survey.
 
 ``` r
 p <- predict(fit, newdata = qcs_grid)