diff --git a/NAMESPACE b/NAMESPACE
index 64a8f5365..0782892d6 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -202,7 +202,6 @@ export(bag_mars)
 export(bag_mlp)
 export(bag_tree)
 export(bart)
-export(bartMachine_interval_calc)
 export(boost_tree)
 export(case_weights_allowed)
 export(cforest_train)
diff --git a/R/arguments.R b/R/arguments.R
index 156916391..3142ce35a 100644
--- a/R/arguments.R
+++ b/R/arguments.R
@@ -258,7 +258,7 @@ make_form_call <- function(object, env = NULL) {
 }
 
 # TODO we need something to indicate that case weights are being used.
-make_xy_call <- function(object, target, env) {
+make_xy_call <- function(object, target, env, call = rlang::caller_env()) {
   fit_args <- object$method$fit$args
   uses_weights <- has_weights(env)
 
@@ -283,7 +283,7 @@ make_xy_call <- function(object, target, env) {
       data.frame = rlang::expr(maybe_data_frame(x)),
       matrix = rlang::expr(maybe_matrix(x)),
       dgCMatrix = rlang::expr(maybe_sparse_matrix(x)),
-      cli::cli_abort("Invalid data type target: {target}.")
+      cli::cli_abort("Invalid data type target: {target}.", call = call)
     )
   if (uses_weights) {
     object$method$fit$args[[ unname(data_args["weights"]) ]] <- rlang::expr(weights)
diff --git a/R/autoplot.R b/R/autoplot.R
index 5ace8f2e0..a880ed4c5 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -41,14 +41,15 @@ autoplot.glmnet <- function(object, ..., min_penalty = 0, best_penalty = NULL,
 }
 
 
-map_glmnet_coefs <- function(x) {
+map_glmnet_coefs <- function(x, call = rlang::caller_env()) {
   coefs <- coef(x)
   # If parsnip is used to fit the model, glmnet should be attached and this will
   # work. If an object is loaded from a new session, they will need to load the
   # package.
   if (is.null(coefs)) {
     cli::cli_abort(
-      "Please load the {.pkg glmnet} package before running {.fun autoplot}."
+      "Please load the {.pkg glmnet} package before running {.fun autoplot}.",
+      call = call
     )
   }
   p <- x$dim[1]
@@ -89,9 +90,10 @@ top_coefs <- function(x, top_n = 5) {
     dplyr::slice(seq_len(top_n))
 }
 
-autoplot_glmnet <- function(x, min_penalty = 0, best_penalty = NULL, top_n = 3L, ...) {
+autoplot_glmnet <- function(x, min_penalty = 0, best_penalty = NULL, top_n = 3L,
+                            call = rlang::caller_env(), ...) {
   tidy_coefs <-
-    map_glmnet_coefs(x) %>%
+    map_glmnet_coefs(x, call = call) %>%
     dplyr::filter(penalty >= min_penalty)
 
   actual_min_penalty <- min(tidy_coefs$penalty)
diff --git a/R/bart.R b/R/bart.R
index 0070f702e..f4e54657c 100644
--- a/R/bart.R
+++ b/R/bart.R
@@ -130,61 +130,13 @@ update.bart <-
     )
   }
 
-
 #' Developer functions for predictions via BART models
-#' @export
-#' @keywords internal
 #' @name bart-internal
 #' @inheritParams predict.model_fit
 #' @param obj A parsnip object.
-#' @param ci Confidence (TRUE) or prediction interval (FALSE)
 #' @param level Confidence level.
 #' @param std_err Attach column for standard error of prediction or not.
-bartMachine_interval_calc <- function(new_data, obj, ci = TRUE, level = 0.95) {
-  if (obj$spec$mode == "classification") {
-    cli::cli_abort(
-      "Prediction intervals are not possible for classification"
-    )
-  }
-  get_std_err <- obj$spec$method$pred$pred_int$extras$std_error
-
-  if (ci) {
-    cl <-
-      rlang::call2(
-        "calc_credible_intervals",
-        .ns = "bartMachine",
-        bart_machine = rlang::expr(obj$fit),
-        new_data = rlang::expr(new_data),
-        ci_conf = level
-      )
-
-  } else {
-    cl <-
-      rlang::call2(
-        "calc_prediction_intervals",
-        .ns = "bartMachine",
-        bart_machine = rlang::expr(obj$fit),
-        new_data = rlang::expr(new_data),
-        pi_conf = level
-      )
-  }
-  res <- rlang::eval_tidy(cl)
-  if (!ci) {
-    if (get_std_err) {
-      .std_error <- apply(res$all_prediction_samples, 1, stats::sd, na.rm = TRUE)
-    }
-    res <- res$interval
-  }
-  res <- tibble::as_tibble(res)
-  names(res) <- c(".pred_lower", ".pred_upper")
-  if (!ci & get_std_err) {
-    res$.std_err <- .std_error
-  }
-  res
-}
-
 #' @export
-#' @rdname bart-internal
 #' @keywords internal
 dbart_predict_calc <- function(obj, new_data, type, level = 0.95, std_err = FALSE) {
   types <- c("numeric", "class", "prob", "conf_int", "pred_int")
diff --git a/R/condense_control.R b/R/condense_control.R
index a5b49bd97..d7b2f984f 100644
--- a/R/condense_control.R
+++ b/R/condense_control.R
@@ -10,6 +10,10 @@
 #'
 #' @return A control object with the same elements and classes of `ref`, with
 #'   values of `x`.
+#' @param call The execution environment of a currently running function, e.g.
+#'   `caller_env()`. The function will be mentioned in error messages as the
+#'   source of the error. See the call argument of [rlang::abort()] for more
+#'   information.
 #' @keywords internal
 #' @export
 #'
@@ -20,16 +24,17 @@
 #'
 #' ctrl <- condense_control(ctrl, control_parsnip())
 #' str(ctrl)
-condense_control <- function(x, ref) {
+condense_control <- function(x, ref, ..., call = rlang::caller_env()) {
+  check_dots_empty()
   mismatch <- setdiff(names(ref), names(x))
   if (length(mismatch)) {
     cli::cli_abort(
       c(
-        "Object of class {.cls class(x)[1]} cannot be coerced to
-         object of class {.cls class(ref)[1]}.",
+        "{.obj_type_friendly {x}} cannot be coerced to {.obj_type_friendly {ref}}.",
         "i" = "{cli::qty(mismatch)} The argument{?s} {.arg {mismatch}}
                {?is/are} missing."
-      )
+      ),
+      call = call
     )
   }
   res <- x[names(ref)]
diff --git a/R/contr_one_hot.R b/R/contr_one_hot.R
index 7ea1115ae..00cdb3484 100644
--- a/R/contr_one_hot.R
+++ b/R/contr_one_hot.R
@@ -3,7 +3,8 @@
 #' This contrast function produces a model matrix with indicator columns for
 #' each level of each factor.
 #'
-#' @param n A vector of character factor levels or the number of unique levels.
+#' @param n A vector of character factor levels (of length >=1) or the number
+#' of unique levels (>= 1).
 #' @param contrasts This argument is for backwards compatibility and only the
 #'   default of `TRUE` is supported.
 #' @param sparse This argument is for backwards compatibility and only the
@@ -24,9 +25,13 @@ contr_one_hot <- function(n, contrasts = TRUE, sparse = FALSE) {
   }
 
   if (is.character(n)) {
+    if (length(n) < 1) {
+      cli::cli_abort("{.arg n} cannot be empty.")
+    }
     names <- n
     n <- length(names)
   } else if (is.numeric(n)) {
+    check_number_whole(n, min = 1)
     n <- as.integer(n)
 
     if (length(n) != 1L) {
@@ -35,7 +40,7 @@ contr_one_hot <- function(n, contrasts = TRUE, sparse = FALSE) {
 
     names <- as.character(seq_len(n))
   } else {
-    cli::cli_abort("{.arg n} must be a character vector or an integer of size 1.")
+    check_number_whole(n, min = 1)
   }
 
   out <- diag(n)
diff --git a/R/convert_data.R b/R/convert_data.R
index 6cf1e8d47..98615cab0 100644
--- a/R/convert_data.R
+++ b/R/convert_data.R
@@ -40,18 +40,21 @@
                                     na.action = na.omit,
                                     indicators = "traditional",
                                     composition = "data.frame",
-                                    remove_intercept = TRUE) {
+                                    remove_intercept = TRUE,
+                                    call = rlang::caller_env()) {
   if (!(composition %in% c("data.frame", "matrix", "dgCMatrix"))) {
     cli::cli_abort(
       "{.arg composition} should be either {.val data.frame}, {.val matrix}, or
-      {.val dgCMatrix}."
+      {.val dgCMatrix}.",
+      call = call
     )
   }
 
   if (sparsevctrs::has_sparse_elements(data)) {
     cli::cli_abort(
-      "Sparse data cannot be used with formula interface. Please use 
-      {.fn fit_xy} instead."
+      "Sparse data cannot be used with formula interface. Please use
+      {.fn fit_xy} instead.",
+      call = call
     )
   }
 
@@ -84,7 +87,7 @@
 
   w <- as.vector(model.weights(mod_frame))
   if (!is.null(w) && !is.numeric(w)) {
-    cli::cli_abort("{.arg weights} must be a numeric vector.")
+    cli::cli_abort("{.arg weights} must be a numeric vector.", call = call)
   }
 
   # TODO: Do we actually use the offset when fitting?
@@ -175,10 +178,12 @@
 .convert_form_to_xy_new <- function(object,
                                     new_data,
                                     na.action = na.pass,
-                                    composition = "data.frame") {
+                                    composition = "data.frame",
+                                    call = rlang::caller_env()) {
   if (!(composition %in% c("data.frame", "matrix"))) {
     cli::cli_abort(
-      "{.arg composition} should be either {.val data.frame} or {.val matrix}."
+      "{.arg composition} should be either {.val data.frame} or {.val matrix}.",
+      call = call
     )
   }
 
@@ -244,9 +249,10 @@
                                     y,
                                     weights = NULL,
                                     y_name = "..y",
-                                    remove_intercept = TRUE) {
+                                    remove_intercept = TRUE,
+                                    call = rlang::caller_env()) {
   if (is.vector(x)) {
-    cli::cli_abort("{.arg x} cannot be a vector.")
+    cli::cli_abort("{.arg x} cannot be a vector.", call = call)
   }
 
   if (remove_intercept) {
@@ -279,10 +285,10 @@
 
   if (!is.null(weights)) {
     if (!is.numeric(weights)) {
-      cli::cli_abort("{.arg weights} must be a numeric vector.")
+      cli::cli_abort("{.arg weights} must be a numeric vector.", call = call)
     }
     if (length(weights) != nrow(x)) {
-      cli::cli_abort("{.arg weights} should have {nrow(x)} elements.")
+      cli::cli_abort("{.arg weights} should have {nrow(x)} elements.", call = call)
     }
 
     form <- patch_formula_environment_with_case_weights(
diff --git a/R/descriptors.R b/R/descriptors.R
index 0d94d14a9..34640d949 100644
--- a/R/descriptors.R
+++ b/R/descriptors.R
@@ -103,22 +103,23 @@ NULL
 
 # Descriptor retrievers --------------------------------------------------------
 
-get_descr_form <- function(formula, data) {
+get_descr_form <- function(formula, data, call = rlang::caller_env()) {
   if (inherits(data, "tbl_spark")) {
     res <- get_descr_spark(formula, data)
   } else {
-    res <- get_descr_df(formula, data)
+    res <- get_descr_df(formula, data, call = call)
   }
   res
 }
 
-get_descr_df <- function(formula, data) {
+get_descr_df <- function(formula, data, call = rlang::caller_env()) {
 
   tmp_dat <-
     .convert_form_to_xy_fit(formula,
                             data,
                             indicators = "none",
-                            remove_intercept = TRUE)
+                            remove_intercept = TRUE,
+                            call = call)
 
   if(is.factor(tmp_dat$y)) {
     .lvls <- function() {
@@ -136,7 +137,8 @@ get_descr_df <- function(formula, data) {
         formula,
         data,
         indicators = "traditional",
-        remove_intercept = TRUE
+        remove_intercept = TRUE,
+        call = call
       )$x
     )
   }
@@ -263,7 +265,7 @@ get_descr_spark <- function(formula, data) {
   )
 }
 
-get_descr_xy <- function(x, y) {
+get_descr_xy <- function(x, y, call = rlang::caller_env()) {
 
   .lvls <- if (is.factor(y)) {
     function() table(y, dnn = NULL)
@@ -291,7 +293,7 @@ get_descr_xy <- function(x, y) {
   }
 
   .dat <- function() {
-    .convert_xy_to_form_fit(x, y, remove_intercept = TRUE)$data
+    .convert_xy_to_form_fit(x, y, remove_intercept = TRUE, call = call)$data
   }
 
   .x <- function() {
diff --git a/R/fit.R b/R/fit.R
index 9925436da..5f7416aa9 100644
--- a/R/fit.R
+++ b/R/fit.R
@@ -157,7 +157,7 @@ fit.model_spec <-
     }
 
     if (all(c("x", "y") %in% names(dots))) {
-      cli::cli_abort("`fit.model_spec()` is for the formula methods. Use `fit_xy()` instead.")
+      cli::cli_abort("{.fn fit.model_spec} is for the formula methods. Use {.fn fit_xy} instead.")
     }
     cl <- match.call(expand.dots = TRUE)
     # Create an environment with the evaluated argument objects. This will be
@@ -307,7 +307,8 @@ fit_xy.model_spec <-
 
     if (object$engine == "spark") {
       cli::cli_abort(
-        "spark objects can only be used with the formula interface to {.fn fit} with a spark data object."
+        "spark objects can only be used with the formula interface to {.fn fit}
+        with a spark data object."
       )
     }
 
diff --git a/R/fit_helpers.R b/R/fit_helpers.R
index ca342b31f..168ea8e44 100644
--- a/R/fit_helpers.R
+++ b/R/fit_helpers.R
@@ -27,7 +27,7 @@ form_form <-
 
     # if descriptors are needed, update descr_env with the calculated values
     if (requires_descrs(object)) {
-      data_stats <- get_descr_form(env$formula, env$data)
+      data_stats <- get_descr_form(env$formula, env$data, call = call)
       scoped_descrs(data_stats)
     }
 
@@ -86,7 +86,7 @@ xy_xy <- function(object,
 
   # if descriptors are needed, update descr_env with the calculated values
   if (requires_descrs(object)) {
-    data_stats <- get_descr_xy(env$x, env$y)
+    data_stats <- get_descr_xy(env$x, env$y, call = call)
     scoped_descrs(data_stats)
   }
 
@@ -96,7 +96,7 @@ xy_xy <- function(object,
   # sub in arguments to actual syntax for corresponding engine
   object <- translate(object, engine = object$engine)
 
-  fit_call <- make_xy_call(object, target, env)
+  fit_call <- make_xy_call(object, target, env, call)
 
   res <- list(lvl = levels(env$y), spec = object)
 
@@ -141,7 +141,8 @@ form_xy <- function(object, control, env,
     ...,
     composition = target,
     indicators = indicators,
-    remove_intercept = remove_intercept
+    remove_intercept = remove_intercept,
+    call = call
   )
   env$x <- data_obj$x
   env$y <- data_obj$y
diff --git a/R/glmnet-engines.R b/R/glmnet-engines.R
index 296468e49..2d31bee3c 100644
--- a/R/glmnet-engines.R
+++ b/R/glmnet-engines.R
@@ -357,18 +357,21 @@ format_glmnet_multinom_class <- function(pred, penalty, lvl, n_obs) {
 #' @rdname glmnet_helpers
 #' @keywords internal
 #' @export
-.check_glmnet_penalty_fit <- function(x) {
+.check_glmnet_penalty_fit <- function(x, call = rlang::caller_env()) {
   pen <- rlang::eval_tidy(x$args$penalty)
 
   if (length(pen) != 1) {
-    cli::cli_abort(c(
-      "x" = "For the glmnet engine, {.arg penalty} must be a single number
-      (or a value of {.fn tune}).",
-      "!" = "There are {length(pen)} value{?s} for {.arg penalty}.",
-      "i" = "To try multiple values for total regularization, use the
-      {.pkg tune} package.",
-      "i" = "To predict multiple penalties, use {.fn multi_predict}."
-    ))
+    cli::cli_abort(
+      c(
+        "x" = "For the glmnet engine, {.arg penalty} must be a single number
+        (or a value of {.fn tune}).",
+        "!" = "There are {length(pen)} value{?s} for {.arg penalty}.",
+        "i" = "To try multiple values for total regularization, use the
+        {.pkg tune} package.",
+        "i" = "To predict multiple penalties, use {.fn multi_predict}."
+      ),
+      call = call
+    )
   }
 }
 
@@ -379,7 +382,8 @@ format_glmnet_multinom_class <- function(pred, penalty, lvl, n_obs) {
 #' @rdname glmnet_helpers
 #' @keywords internal
 #' @export
-.check_glmnet_penalty_predict <- function(penalty = NULL, object, multi = FALSE) {
+.check_glmnet_penalty_predict <- function(penalty = NULL, object, multi = FALSE,
+                                          call = rlang::caller_env()) {
   if (is.null(penalty)) {
     penalty <- object$fit$lambda
   }
@@ -387,19 +391,25 @@ format_glmnet_multinom_class <- function(pred, penalty, lvl, n_obs) {
   # when using `predict()`, allow for a single lambda
   if (!multi) {
     if (length(penalty) != 1) {
-      cli::cli_abort(c(
-        "{.arg penalty} should be a single numeric value.",
-        "i" = "{.fn multi_predict} can be used to get multiple predictions per row of data."
-      ))
+      cli::cli_abort(
+        c(
+          "{.arg penalty} should be a single numeric value.",
+          "i" = "{.fn multi_predict} can be used to get multiple predictions per row of data."
+        ),
+        call = call
+      )
     }
   }
 
   if (length(object$fit$lambda) == 1 && penalty != object$fit$lambda) {
-    cli::cli_abort(c(
-      "The glmnet model was fit with a single penalty value of
+    cli::cli_abort(
+      c(
+        "The glmnet model was fit with a single penalty value of
       {.arg object$fit$lambda}. Predicting with a value of {.arg penalty}
       will give incorrect results from `glmnet()`."
-    ))
+      ),
+      call = call
+    )
   }
 
   penalty
diff --git a/R/mars.R b/R/mars.R
index 49321b3f7..6decfb8ce 100644
--- a/R/mars.R
+++ b/R/mars.R
@@ -161,21 +161,23 @@ multi_predict._earth <-
         object$fit$call[[i]] <- eval_tidy(object$fit$call[[i]])
     }
 
-    msg <- 
-      c("x" = "Please use {.code keepxy = TRUE} as an option to enable submodel 
+    msg <-
+      c("x" = "Please use {.code keepxy = TRUE} as an option to enable submodel
                      predictions with earth.")
     if (any(names(object$fit$call) == "keepxy")) {
-       if (!isTRUE(object$fit$call$keepxy))
-         cli::cli_abort(msg)
+      if (!isTRUE(object$fit$call$keepxy)) {
+        cli::cli_abort(msg)
+      }
     } else {
       cli::cli_abort(msg)
     }
 
     if (is.null(type)) {
-      if (object$spec$mode == "classification")
+      if (object$spec$mode == "classification") {
         type <- "class"
-      else
+      } else {
         type <- "numeric"
+      }
     }
 
     res <-
diff --git a/R/misc.R b/R/misc.R
index dfec898a6..7eb22b4f9 100644
--- a/R/misc.R
+++ b/R/misc.R
@@ -415,21 +415,21 @@ check_outcome <- function(y, spec) {
 #' @export
 #' @keywords internal
 #' @rdname add_on_exports
-check_final_param <- function(x) {
+check_final_param <- function(x, call = rlang::caller_env()) {
   if (is.null(x)) {
     return(invisible(x))
   }
   if (!is.list(x) & !tibble::is_tibble(x)) {
-    cli::cli_abort("The parameter object should be a list or tibble.")
+    cli::cli_abort("The parameter object should be a list or tibble.", call = call)
   }
   if (tibble::is_tibble(x) && nrow(x) > 1) {
-    cli::cli_abort("The parameter tibble should have a single row.")
+    cli::cli_abort("The parameter tibble should have a single row.", call = call)
   }
   if (tibble::is_tibble(x)) {
     x <- as.list(x)
   }
   if (length(names) == 0 || any(names(x) == "")) {
-    cli::cli_abort("All values in {.arg parameters} should have a name.")
+    cli::cli_abort("All values in {.arg parameters} should have a name.", call = call)
   }
 
   invisible(x)
@@ -438,7 +438,7 @@ check_final_param <- function(x) {
 #' @export
 #' @keywords internal
 #' @rdname add_on_exports
-update_main_parameters <- function(args, param) {
+update_main_parameters <- function(args, param, call = rlang::caller_env()) {
   if (length(param) == 0) {
     return(args)
   }
@@ -451,7 +451,8 @@ update_main_parameters <- function(args, param) {
   extra_args <- names(param)[has_extra_args]
   if (any(has_extra_args)) {
     cli::cli_abort(
-      "Argument{?s} {.arg {extra_args}} {?is/are} not a main argument."
+      "Argument{?s} {.arg {extra_args}} {?is/are} not a main argument.",
+      call = call
     )
   }
   param <- param[!has_extra_args]
diff --git a/R/mlp.R b/R/mlp.R
index b35dbe32c..5080c555e 100644
--- a/R/mlp.R
+++ b/R/mlp.R
@@ -146,9 +146,10 @@ check_args.mlp <- function(object, call = rlang::caller_env()) {
 
 # keras wrapper for feed-forward nnet
 
-class2ind <- function (x, drop2nd = FALSE) {
-  if (!is.factor(x))
-    cli::cli_abort(c("x" = "{.arg x} should be a factor."))
+class2ind <- function (x, drop2nd = FALSE, call = rlang::caller_env()) {
+  if (!is.factor(x)) {
+    cli::cli_abort(c("x" = "{.arg x} should be a {cls factor} not {.obj_type_friendly {x}."))
+  }
   y <- model.matrix( ~ x - 1)
   colnames(y) <- gsub("^x", "", colnames(y))
   attributes(y)$assign <- NULL
diff --git a/R/predict_class.R b/R/predict_class.R
index 3c8fe69a6..d11f79f36 100644
--- a/R/predict_class.R
+++ b/R/predict_class.R
@@ -10,13 +10,15 @@
 #' @export
 predict_class.model_fit <- function(object, new_data, ...) {
   if (object$spec$mode != "classification") {
-    cli::cli_abort("{.fun predict.model_fit} is for predicting factor outcomes.")
+    cli::cli_abort("{.fun predict.model_fit} is for predicting factor outcomes.",
+                   call = rlang::call2("predict"))
   }
 
   check_spec_pred_type(object, "class")
 
   if (inherits(object$fit, "try-error")) {
-    cli::cli_warn("Model fit failed; cannot make predictions.")
+    cli::cli_warn("Model fit failed; cannot make predictions.",
+                  call = rlang::call2("predict"))
     return(NULL)
   }
 
diff --git a/R/predict_classprob.R b/R/predict_classprob.R
index 86bea49d3..7642ae3d3 100644
--- a/R/predict_classprob.R
+++ b/R/predict_classprob.R
@@ -6,7 +6,8 @@
 #' @export
 predict_classprob.model_fit <- function(object, new_data, ...) {
   if (object$spec$mode != "classification") {
-    cli::cli_abort("{.fun predict.model_fit()} is for predicting factor outcomes.")
+    cli::cli_abort("{.fun predict.model_fit()} is for predicting factor outcomes.",
+                   call = rlang::call2("predict"))
   }
 
   check_spec_pred_type(object, "prob", call = caller_env())
@@ -36,7 +37,8 @@ predict_classprob.model_fit <- function(object, new_data, ...) {
 
   # check and sort names
   if (!is.data.frame(res) & !inherits(res, "tbl_spark")) {
-    cli::cli_abort("The was a problem with the probability predictions.")
+    cli::cli_abort("The was a problem with the probability predictions.",
+                   call = rlang::call2("predict"))
   }
 
   if (!is_tibble(res) & !inherits(res, "tbl_spark")) {
diff --git a/R/predict_numeric.R b/R/predict_numeric.R
index 6f8aed916..509c39ef0 100644
--- a/R/predict_numeric.R
+++ b/R/predict_numeric.R
@@ -11,7 +11,8 @@ predict_numeric.model_fit <- function(object, new_data, ...) {
         "{.fun predict_numeric} is for predicting numeric outcomes.",
         "i" = "Use {.fun predict_class} or {.fun predict_classprob} for
                classification models."
-      )
+      ),
+      call = rlang::call2("predict")
     )
   }
 
diff --git a/R/predict_quantile.R b/R/predict_quantile.R
index 6a8b5060b..54d6461ff 100644
--- a/R/predict_quantile.R
+++ b/R/predict_quantile.R
@@ -37,7 +37,8 @@ predict_quantile.model_fit <- function(object,
   if (object$spec$mode == "quantile regression") {
     if (!is.null(quantile_levels)) {
       cli::cli_abort("When the mode is {.val quantile regression},
-                     {.arg quantile_levels} are specified by {.fn set_mode}.")
+                     {.arg quantile_levels} are specified by {.fn set_mode}.",
+                     call = rlang::call2("predict"))
     }
   } else {
     if (is.null(quantile_levels)) {
diff --git a/R/predict_time.R b/R/predict_time.R
index 769b7a578..616cb29aa 100644
--- a/R/predict_time.R
+++ b/R/predict_time.R
@@ -11,7 +11,8 @@ predict_time.model_fit <- function(object, new_data, ...) {
         "{.fun predict_time} is for predicting time outcomes.",
         "i" = "Use {.fun predict_class} or {.fun predict_classprob} for
                classification models."
-      )
+      ),
+      call = rlang::call2("predict")
     )
   }
 
diff --git a/R/rand_forest_data.R b/R/rand_forest_data.R
index ef548dda3..598bceaa9 100644
--- a/R/rand_forest_data.R
+++ b/R/rand_forest_data.R
@@ -66,7 +66,8 @@ ranger_confint <- function(object, new_data, ...) {
     } else {
       cli::cli_abort(
         "Cannot compute confidence intervals for a ranger forest
-         of type {.val {object$fit$forest$treetype}}."
+         of type {.val {object$fit$forest$treetype}}.",
+        call = rlang::call2("predict")
       )
     }
   }
diff --git a/R/translate.R b/R/translate.R
index eb2ef7158..6c0fc2881 100644
--- a/R/translate.R
+++ b/R/translate.R
@@ -176,6 +176,7 @@ add_methods <- function(x, engine) {
 #' or in a list that can facilitate renaming grid objects?
 #' @return A tibble with columns `user`, `parsnip`, and `engine`, or a list
 #' with named character vectors `user_to_parsnip` and `parsnip_to_engine`.
+#' @keywords internal
 #' @examplesIf !parsnip:::is_cran_check()
 #' mod <-
 #'  linear_reg(penalty = tune("regularization"), mixture = tune()) %>%
diff --git a/R/update.R b/R/update.R
index 3dd890fb4..02cf1d53f 100644
--- a/R/update.R
+++ b/R/update.R
@@ -64,10 +64,10 @@ update_spec <- function(object, parameters, args_enquo_list, fresh, cls, ...,
   eng_args <- update_engine_parameters(object$eng_args, fresh, ...)
 
   if (!is.null(parameters)) {
-    parameters <- check_final_param(parameters)
+    parameters <- check_final_param(parameters, call = call)
   }
 
-  args <- update_main_parameters(args_enquo_list, parameters)
+  args <- update_main_parameters(args_enquo_list, parameters, call = call)
 
   if (fresh) {
     object$args <- args
diff --git a/man/add_on_exports.Rd b/man/add_on_exports.Rd
index 2f1d62a14..09c3434fb 100644
--- a/man/add_on_exports.Rd
+++ b/man/add_on_exports.Rd
@@ -34,9 +34,9 @@ new_model_spec(
   user_specified_engine = TRUE
 )
 
-check_final_param(x)
+check_final_param(x, call = rlang::caller_env())
 
-update_main_parameters(args, param)
+update_main_parameters(args, param, call = rlang::caller_env())
 
 update_engine_parameters(eng_args, fresh, ...)
 
diff --git a/man/bart-internal.Rd b/man/bart-internal.Rd
index 2665bd380..4056cd69f 100644
--- a/man/bart-internal.Rd
+++ b/man/bart-internal.Rd
@@ -2,28 +2,23 @@
 % Please edit documentation in R/bart.R
 \name{bart-internal}
 \alias{bart-internal}
-\alias{bartMachine_interval_calc}
 \alias{dbart_predict_calc}
 \title{Developer functions for predictions via BART models}
 \usage{
-bartMachine_interval_calc(new_data, obj, ci = TRUE, level = 0.95)
-
 dbart_predict_calc(obj, new_data, type, level = 0.95, std_err = FALSE)
 }
 \arguments{
-\item{new_data}{A rectangular data object, such as a data frame.}
-
 \item{obj}{A parsnip object.}
 
-\item{ci}{Confidence (TRUE) or prediction interval (FALSE)}
-
-\item{level}{Confidence level.}
+\item{new_data}{A rectangular data object, such as a data frame.}
 
 \item{type}{A single character value or \code{NULL}. Possible values
 are \code{"numeric"}, \code{"class"}, \code{"prob"}, \code{"conf_int"}, \code{"pred_int"},
 \code{"quantile"}, \code{"time"}, \code{"hazard"}, \code{"survival"}, or \code{"raw"}. When \code{NULL},
 \code{predict()} will choose an appropriate value based on the model's mode.}
 
+\item{level}{Confidence level.}
+
 \item{std_err}{Attach column for standard error of prediction or not.}
 }
 \description{
diff --git a/man/condense_control.Rd b/man/condense_control.Rd
index 326dae09b..d347bcd3e 100644
--- a/man/condense_control.Rd
+++ b/man/condense_control.Rd
@@ -4,13 +4,18 @@
 \alias{condense_control}
 \title{Condense control object into strictly smaller control object}
 \usage{
-condense_control(x, ref)
+condense_control(x, ref, ..., call = rlang::caller_env())
 }
 \arguments{
 \item{x}{A control object to be condensed.}
 
 \item{ref}{A control object that is used to determine what element should be
 kept.}
+
+\item{call}{The execution environment of a currently running function, e.g.
+\code{caller_env()}. The function will be mentioned in error messages as the
+source of the error. See the call argument of \code{\link[rlang:abort]{rlang::abort()}} for more
+information.}
 }
 \value{
 A control object with the same elements and classes of \code{ref}, with
diff --git a/man/contr_one_hot.Rd b/man/contr_one_hot.Rd
index f57e589fc..57ff6654c 100644
--- a/man/contr_one_hot.Rd
+++ b/man/contr_one_hot.Rd
@@ -7,7 +7,8 @@
 contr_one_hot(n, contrasts = TRUE, sparse = FALSE)
 }
 \arguments{
-\item{n}{A vector of character factor levels or the number of unique levels.}
+\item{n}{A vector of character factor levels (of length >=1) or the number
+of unique levels (>= 1).}
 
 \item{contrasts}{This argument is for backwards compatibility and only the
 default of \code{TRUE} is supported.}
diff --git a/man/convert_helpers.Rd b/man/convert_helpers.Rd
index d25f830bd..583e91b46 100644
--- a/man/convert_helpers.Rd
+++ b/man/convert_helpers.Rd
@@ -14,14 +14,16 @@
   na.action = na.omit,
   indicators = "traditional",
   composition = "data.frame",
-  remove_intercept = TRUE
+  remove_intercept = TRUE,
+  call = rlang::caller_env()
 )
 
 .convert_form_to_xy_new(
   object,
   new_data,
   na.action = na.pass,
-  composition = "data.frame"
+  composition = "data.frame",
+  call = rlang::caller_env()
 )
 
 .convert_xy_to_form_fit(
@@ -29,7 +31,8 @@
   y,
   weights = NULL,
   y_name = "..y",
-  remove_intercept = TRUE
+  remove_intercept = TRUE,
+  call = rlang::caller_env()
 )
 
 .convert_xy_to_form_new(object, new_data)
diff --git a/man/dot-model_param_name_key.Rd b/man/dot-model_param_name_key.Rd
index 31372ae8e..cfd1640d8 100644
--- a/man/dot-model_param_name_key.Rd
+++ b/man/dot-model_param_name_key.Rd
@@ -42,3 +42,4 @@ grid \%>\%
   dplyr::rename(!!!rn$parsnip_to_engine)
 \dontshow{\}) # examplesIf}
 }
+\keyword{internal}
diff --git a/man/glmnet_helpers.Rd b/man/glmnet_helpers.Rd
index e6fcfc3aa..246fb1f20 100644
--- a/man/glmnet_helpers.Rd
+++ b/man/glmnet_helpers.Rd
@@ -5,9 +5,14 @@
 \alias{.check_glmnet_penalty_predict}
 \title{Helper functions for checking the penalty of glmnet models}
 \usage{
-.check_glmnet_penalty_fit(x)
+.check_glmnet_penalty_fit(x, call = rlang::caller_env())
 
-.check_glmnet_penalty_predict(penalty = NULL, object, multi = FALSE)
+.check_glmnet_penalty_predict(
+  penalty = NULL,
+  object,
+  multi = FALSE,
+  call = rlang::caller_env()
+)
 }
 \arguments{
 \item{x}{An object of class \code{model_spec}.}
diff --git a/tests/testthat/_snaps/condense_control.md b/tests/testthat/_snaps/condense_control.md
index 19f908d21..7e9277238 100644
--- a/tests/testthat/_snaps/condense_control.md
+++ b/tests/testthat/_snaps/condense_control.md
@@ -3,7 +3,16 @@
     Code
       condense_control(control_parsnip(), ctrl)
     Condition
-      Error in `condense_control()`:
-      ! Object of class <class(x)[1]> cannot be coerced to object of class <class(ref)[1]>.
+      Error:
+      ! a <control_parsnip> object cannot be coerced to a <control_parsnip> object.
+      i The arguments `allow_par` and `anotherone` are missing.
+
+---
+
+    Code
+      control_test(ctrl)
+    Condition
+      Error in `control_test()`:
+      ! a <control_parsnip> object cannot be coerced to a <control_parsnip> object.
       i The arguments `allow_par` and `anotherone` are missing.
 
diff --git a/tests/testthat/_snaps/contr_one_hot.md b/tests/testthat/_snaps/contr_one_hot.md
new file mode 100644
index 000000000..f677fd5a6
--- /dev/null
+++ b/tests/testthat/_snaps/contr_one_hot.md
@@ -0,0 +1,48 @@
+# one-hot encoding contrasts
+
+    Code
+      contr_one_hot(character(0))
+    Condition
+      Error in `contr_one_hot()`:
+      ! `n` cannot be empty.
+
+---
+
+    Code
+      contr_one_hot(-1)
+    Condition
+      Error in `contr_one_hot()`:
+      ! `n` must be a whole number larger than or equal to 1, not the number -1.
+
+---
+
+    Code
+      contr_one_hot(list())
+    Condition
+      Error in `contr_one_hot()`:
+      ! `n` must be a whole number, not an empty list.
+
+---
+
+    Code
+      contr_one_hot(2, contrast = FALSE)
+    Condition
+      Warning:
+      `contrasts = FALSE` not implemented for `contr_one_hot()`.
+    Output
+        1 2
+      1 1 0
+      2 0 1
+
+---
+
+    Code
+      contr_one_hot(2, sparse = TRUE)
+    Condition
+      Warning:
+      `sparse = TRUE` not implemented for `contr_one_hot()`.
+    Output
+        1 2
+      1 1 0
+      2 0 1
+
diff --git a/tests/testthat/_snaps/convert_data.md b/tests/testthat/_snaps/convert_data.md
index bc4c5606b..d864f282a 100644
--- a/tests/testthat/_snaps/convert_data.md
+++ b/tests/testthat/_snaps/convert_data.md
@@ -21,7 +21,7 @@
       .convert_form_to_xy_fit(mpg ~ ., data = mtcars, composition = "tibble",
       indicators = "traditional", remove_intercept = TRUE)
     Condition
-      Error in `.convert_form_to_xy_fit()`:
+      Error:
       ! `composition` should be either "data.frame", "matrix", or "dgCMatrix".
 
 ---
@@ -30,7 +30,7 @@
       .convert_form_to_xy_fit(mpg ~ ., data = mtcars, weights = letters[1:nrow(mtcars)],
       indicators = "traditional", remove_intercept = TRUE)
     Condition
-      Error in `.convert_form_to_xy_fit()`:
+      Error:
       ! `weights` must be a numeric vector.
 
 ---
@@ -38,7 +38,7 @@
     Code
       .convert_xy_to_form_fit(mtcars$disp, mtcars$mpg, remove_intercept = TRUE)
     Condition
-      Error in `.convert_xy_to_form_fit()`:
+      Error:
       ! `x` cannot be a vector.
 
 ---
diff --git a/tests/testthat/_snaps/linear_reg_quantreg.md b/tests/testthat/_snaps/linear_reg_quantreg.md
index cba265991..11fbd80e2 100644
--- a/tests/testthat/_snaps/linear_reg_quantreg.md
+++ b/tests/testthat/_snaps/linear_reg_quantreg.md
@@ -4,6 +4,6 @@
       ten_quant_pred <- predict(ten_quant, new_data = sac_test, quantile_levels = (0:
       9) / 9)
     Condition
-      Error in `predict_quantile()`:
+      Error in `predict()`:
       ! When the mode is "quantile regression", `quantile_levels` are specified by `set_mode()`.
 
diff --git a/tests/testthat/_snaps/misc.md b/tests/testthat/_snaps/misc.md
index 0bbae37cf..b6b1f918c 100644
--- a/tests/testthat/_snaps/misc.md
+++ b/tests/testthat/_snaps/misc.md
@@ -1,5 +1,13 @@
 # parsnip objects
 
+    Code
+      predict(lm_idea, mtcars)
+    Condition
+      Error in `predict()`:
+      ! You must `fit()` your model specification (`?parsnip::model_spec()`) before you can use `predict()`.
+
+---
+
     Code
       multi_predict(lm_fit, mtcars)
     Condition
diff --git a/tests/testthat/_snaps/sparsevctrs.md b/tests/testthat/_snaps/sparsevctrs.md
index 006fc82b8..30c84b1a9 100644
--- a/tests/testthat/_snaps/sparsevctrs.md
+++ b/tests/testthat/_snaps/sparsevctrs.md
@@ -3,7 +3,7 @@
     Code
       xgb_fit <- fit(spec, avg_price_per_room ~ ., data = hotel_data)
     Condition
-      Error in `.convert_form_to_xy_fit()`:
+      Error in `fit()`:
       ! Sparse data cannot be used with formula interface. Please use `fit_xy()` instead.
 
 # sparse tibble can be passed to `fit() - unsupported
@@ -19,7 +19,7 @@
     Code
       xgb_fit <- fit(spec, avg_price_per_room ~ ., data = hotel_data)
     Condition
-      Error in `.convert_form_to_xy_fit()`:
+      Error in `fit()`:
       ! Sparse data cannot be used with formula interface. Please use `fit_xy()` instead.
 
 # sparse matrix can be passed to `fit() - unsupported
@@ -67,7 +67,7 @@
     Code
       xgb_fit <- fit(spec, avg_price_per_room ~ ., data = hotel_data)
     Condition
-      Error in `.convert_form_to_xy_fit()`:
+      Error in `fit()`:
       ! Sparse data cannot be used with formula interface. Please use `fit_xy()` instead.
 
 # to_sparse_data_frame() is used correctly
diff --git a/tests/testthat/_snaps/translate.md b/tests/testthat/_snaps/translate.md
index edb00ca38..b7e2488f3 100644
--- a/tests/testthat/_snaps/translate.md
+++ b/tests/testthat/_snaps/translate.md
@@ -503,7 +503,7 @@
     Code
       translate_args(basic %>% set_engine("glmnet"))
     Condition
-      Error in `.check_glmnet_penalty_fit()`:
+      Error in `translate()`:
       x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
       ! There are 0 values for `penalty`.
       i To try multiple values for total regularization, use the tune package.
@@ -554,7 +554,7 @@
     Code
       translate_args(mixture %>% set_engine("glmnet"))
     Condition
-      Error in `.check_glmnet_penalty_fit()`:
+      Error in `translate()`:
       x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
       ! There are 0 values for `penalty`.
       i To try multiple values for total regularization, use the tune package.
@@ -687,7 +687,7 @@
     Code
       translate_args(basic %>% set_engine("glmnet"))
     Condition
-      Error in `.check_glmnet_penalty_fit()`:
+      Error in `translate()`:
       x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
       ! There are 0 values for `penalty`.
       i To try multiple values for total regularization, use the tune package.
@@ -826,7 +826,7 @@
     Code
       translate_args(mixture %>% set_engine("glmnet"))
     Condition
-      Error in `.check_glmnet_penalty_fit()`:
+      Error in `translate()`:
       x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
       ! There are 0 values for `penalty`.
       i To try multiple values for total regularization, use the tune package.
@@ -967,7 +967,7 @@
     Code
       translate_args(mixture_v %>% set_engine("glmnet"))
     Condition
-      Error in `.check_glmnet_penalty_fit()`:
+      Error in `translate()`:
       x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
       ! There are 0 values for `penalty`.
       i To try multiple values for total regularization, use the tune package.
@@ -1333,7 +1333,7 @@
     Code
       translate_args(basic %>% set_engine("glmnet"))
     Condition
-      Error in `.check_glmnet_penalty_fit()`:
+      Error in `translate()`:
       x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
       ! There are 0 values for `penalty`.
       i To try multiple values for total regularization, use the tune package.
@@ -1551,7 +1551,7 @@
     Code
       basic_incomplete %>% translate_args()
     Condition
-      Error in `.check_glmnet_penalty_fit()`:
+      Error in `translate()`:
       x For the glmnet engine, `penalty` must be a single number (or a value of `tune()`).
       ! There are 0 values for `penalty`.
       i To try multiple values for total regularization, use the tune package.
diff --git a/tests/testthat/_snaps/update.md b/tests/testthat/_snaps/update.md
index 394aa2a9f..be0b23487 100644
--- a/tests/testthat/_snaps/update.md
+++ b/tests/testthat/_snaps/update.md
@@ -196,7 +196,7 @@
     Code
       expr1 %>% update(param_tibb)
     Condition
-      Error in `update_main_parameters()`:
+      Error in `update()`:
       ! Argument `nlambda` is not a main argument.
 
 ---
@@ -204,7 +204,7 @@
     Code
       expr1 %>% update(param_list)
     Condition
-      Error in `update_main_parameters()`:
+      Error in `update()`:
       ! Argument `nlambda` is not a main argument.
 
 ---
@@ -212,7 +212,7 @@
     Code
       expr1 %>% update(parameters = "wat")
     Condition
-      Error in `check_final_param()`:
+      Error in `update()`:
       ! The parameter object should be a list or tibble.
 
 ---
@@ -220,7 +220,7 @@
     Code
       expr1 %>% update(parameters = tibble::tibble(wat = "wat"))
     Condition
-      Error in `update_main_parameters()`:
+      Error in `update()`:
       ! Argument `wat` is not a main argument.
 
 ---
diff --git a/tests/testthat/test-condense_control.R b/tests/testthat/test-condense_control.R
index 4da0c2d57..345bac5dc 100644
--- a/tests/testthat/test-condense_control.R
+++ b/tests/testthat/test-condense_control.R
@@ -18,4 +18,11 @@ test_that("condense_control works", {
   expect_snapshot(error = TRUE,
     condense_control(control_parsnip(), ctrl)
   )
+
+  # Emulate being called from one of the upstream control_* functions
+  control_test <- function(control = control_parsnip()) {
+    control <- parsnip::condense_control(control_parsnip(), control)
+    invisible(control)
+  }
+  expect_snapshot(error = TRUE, control_test(ctrl))
 })
diff --git a/tests/testthat/test-contr_one_hot.R b/tests/testthat/test-contr_one_hot.R
new file mode 100644
index 000000000..3f1d78857
--- /dev/null
+++ b/tests/testthat/test-contr_one_hot.R
@@ -0,0 +1,19 @@
+test_that('one-hot encoding contrasts', {
+  contr_mat <- contr_one_hot(12)
+  expect_equal(colnames(contr_mat), paste(1:12))
+  expect_equal(rownames(contr_mat), paste(1:12))
+  expect_true(all(apply(contr_mat, 1, sum) == 1))
+  expect_true(all(apply(contr_mat, 2, sum) == 1))
+
+  chr_contr_mat <- contr_one_hot(letters[1:12])
+  expect_equal(colnames(chr_contr_mat), letters[1:12])
+  expect_equal(rownames(chr_contr_mat), letters[1:12])
+  expect_true(all(apply(chr_contr_mat, 1, sum) == 1))
+  expect_true(all(apply(chr_contr_mat, 2, sum) == 1))
+
+  expect_snapshot(contr_one_hot(character(0)), error = TRUE)
+  expect_snapshot(contr_one_hot(-1), error = TRUE)
+  expect_snapshot(contr_one_hot(list()), error = TRUE)
+  expect_snapshot(contr_one_hot(2, contrast = FALSE))
+  expect_snapshot(contr_one_hot(2, sparse = TRUE))
+})
diff --git a/tests/testthat/test-misc.R b/tests/testthat/test-misc.R
index af18a4a7d..e689bcbcf 100644
--- a/tests/testthat/test-misc.R
+++ b/tests/testthat/test-misc.R
@@ -5,6 +5,7 @@ test_that('parsnip objects', {
 
   lm_idea <- linear_reg() %>% set_engine("lm")
   expect_false(has_multi_predict(lm_idea))
+  expect_snapshot(error = TRUE, predict(lm_idea, mtcars))
 
   lm_fit <- fit(lm_idea, mpg ~ ., data = mtcars)
   expect_false(has_multi_predict(lm_fit))