diff --git a/v1/retail_planning/README.md b/v1/retail_planning/README.md
index f79878d0..75f81d26 100644
--- a/v1/retail_planning/README.md
+++ b/v1/retail_planning/README.md
@@ -24,6 +24,13 @@ Retailers face interconnected decisions: which items will sell, which customers
 
 **Then adapt the pattern to your own Snowflake data** using `retail_planning.py` as a reference. It trains three GNNs (sales regression, customer-churn classification, user-article link prediction) against the full Kaggle H&M dataset in Snowflake, aggregates all three signals into an adjusted demand estimate, and feeds that into the same two optimizers. The H&M pipeline is the worked example -- the structure (graph concepts → GNN tasks → aggregation bridge → prescriptive constraints) is what carries over to your own retail, pricing, or demand-planning data.
 
+> [!IMPORTANT]
+> The RelationalAI **predictive reasoner (GNN)** used in this template is in
+> early access. The API surface (`GNN`, `PropertyTransformer`, task
+> relationships) may still change between releases; check the
+> `rai-predictive-modeling` and `rai-predictive-training` skills for the
+> current guidance before adapting to production data.
+
 ## Who this is for
 
 - Data scientists building end-to-end ML-to-optimization pipelines