diff --git a/pipelines/tests/test_end_to_end.sh b/pipelines/tests/test_end_to_end.sh
index 1eabeebd..fba4e736 100755
--- a/pipelines/tests/test_end_to_end.sh
+++ b/pipelines/tests/test_end_to_end.sh
@@ -26,7 +26,7 @@ do
 	       --state $state \
 	       --facility-level-nssp-data-dir "$BASE_DIR/private_data/nssp_etl_gold" \
 	       --state-level-nssp-data-dir "$BASE_DIR/private_data/nssp_state_level_gold" \
-	       --priors-path "$BASE_DIR/test_output/priors.py" \
+	       --priors-path pipelines/priors/prod_priors.py \
 	       --param-data-dir "$BASE_DIR/private_data/prod_param_estimates" \
 	       --output-dir "$BASE_DIR/private_data" \
 	       --n-training-days 60 \
diff --git a/pipelines/tests/test_output/priors.py b/pipelines/tests/test_output/priors.py
deleted file mode 100644
index 4f9d61ab..00000000
--- a/pipelines/tests/test_output/priors.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import jax.numpy as jnp
-import numpyro.distributions as dist
-import pyrenew.transformation as transformation
-from numpyro.infer.reparam import LocScaleReparam
-from pyrenew.randomvariable import DistributionalVariable, TransformedVariable
-
-i0_first_obs_n_rv = DistributionalVariable(
-    "i0_first_obs_n_rv",
-    dist.Beta(1, 10),
-)
-
-initialization_rate_rv = DistributionalVariable(
-    "rate", dist.Normal(0, 0.01), reparam=LocScaleReparam(0)
-)
-
-r_logmean = jnp.log(1)
-r_logsd = jnp.log(jnp.sqrt(2))
-
-log_r_mu_intercept_rv = DistributionalVariable(
-    "log_r_mu_intercept_rv", dist.Normal(r_logmean, r_logsd)
-)
-
-eta_sd_rv = DistributionalVariable(
-    "eta_sd", dist.TruncatedNormal(0.04, 0.02, low=0)
-)
-
-autoreg_rt_rv = DistributionalVariable("autoreg_rt", dist.Beta(2, 40))
-
-
-inf_feedback_strength_rv = TransformedVariable(
-    "inf_feedback",
-    DistributionalVariable(
-        "inf_feedback_raw",
-        dist.LogNormal(jnp.log(50), jnp.log(2)),
-    ),
-    transforms=transformation.AffineTransform(loc=0, scale=-1),
-)
-# Could be reparameterized?
-
-p_ed_visit_mean_rv = DistributionalVariable(
-    "p_ed_visit_mean",
-    dist.Normal(
-        transformation.SigmoidTransform().inv(0.005),
-        0.3,
-    ),
-)  # logit scale
-
-
-p_ed_visit_w_sd_rv = DistributionalVariable(
-    "p_ed_visit_w_sd_sd", dist.TruncatedNormal(0, 0.01, low=0)
-)
-
-
-autoreg_p_ed_visit_rv = DistributionalVariable(
-    "autoreg_p_ed_visit_rv", dist.Beta(1, 100)
-)
-
-ed_visit_wday_effect_rv = TransformedVariable(
-    "ed_visit_wday_effect",
-    DistributionalVariable(
-        "ed_visit_wday_effect_raw",
-        dist.Dirichlet(jnp.array([5, 5, 5, 5, 5, 5, 5])),
-    ),
-    transformation.AffineTransform(loc=0, scale=7),
-)
-
-# Based on looking at some historical posteriors.
-phi_rv = DistributionalVariable("phi", dist.LogNormal(6, 1))