diff --git a/docs/ai/quickstart_fastapi_ai.rst b/docs/ai/quickstart_fastapi_ai.rst
index 958831b8f8b..ada703299cc 100644
--- a/docs/ai/quickstart_fastapi_ai.rst
+++ b/docs/ai/quickstart_fastapi_ai.rst
@@ -6,12 +6,16 @@ Using the built-in RAG
 
 .. edb:split-section::
 
-    In this section we'll use |Gel|'s built-in vector search and
-    retrieval-augmented generation capabilities to decorate our flashcard app
-    with a couple AI features. We're going to create a ``/fetch_similar``
-    endpoint that's going to look up flashcards similar to a text search query,
-    as well as a ``/fetch_rag`` endpoint that's going to enable us to talk to
-    an LLM about the content of our flashcard deck.
+    In this section we'll learn about |Gel's| built-in vector search and
+    retrieval-augmented generation capabilities. We'll be continuing from where
+    we left off in the :ref:`main quickstart <ref_quickstart>`. Feel free to browse the
+    complete flascards app code in this `repo
+    <https://github.com/edgedb/quickstart-fastapi>`_.
+
+    In this tutorial we'll focus on creating a ``/fetch_similar`` endpoint for
+    looking up flashcards similar to a text search query, as well as a
+    ``/fetch_rag`` endpoint that's going to enable us to talk to an LLM about
+    the content of our flashcard deck.
 
     We're going to start with the same schema we left off with in the primary
     quickstart.
@@ -342,5 +346,7 @@ Using the built-in RAG
 
     Congratulations! We've now implemented AI features in our flashcards app.
     Of course, there's more to learn when it comes to using the AI extension.
-    Make sure to check out the Reference manual, or build an LLM-powered search
-    bot from the ground up with the FastAPI Gel AI tutorial.
+    Make sure to check out the :ref:`Reference manual
+    <ref_ai_python_reference>`, or build an LLM-powered search bot from the
+    ground up with the :ref:`FastAPI Gel AI tutorial
+    <ref_guide_fastapi_gelai_searchbot>`.