diff --git a/modules/genai-ecosystem/nav.adoc b/modules/genai-ecosystem/nav.adoc
index facdbf4..5711e5b 100644
--- a/modules/genai-ecosystem/nav.adoc
+++ b/modules/genai-ecosystem/nav.adoc
@@ -5,8 +5,8 @@
 **** xref:google-cloud-demo.adoc[Google Cloud Vertex AI]
 *** Example Projects
 **** xref:llm-graph-builder.adoc[LLM Graph Builder]
-***** xref:llm-graph-builder-deployment.adoc[Deployment]
 ***** xref:llm-graph-builder-features.adoc[Features]
+***** xref:llm-graph-builder-deployment.adoc[Deployment]
 **** xref:rag-demo.adoc[GraphRAG Demo]
 **** xref:neoconverse.adoc[NeoConverse]
 **** xref:genai-stack.adoc[GenAI Stack]
diff --git a/modules/genai-ecosystem/pages/llm-graph-builder.adoc b/modules/genai-ecosystem/pages/llm-graph-builder.adoc
index e1079be..36ea53a 100644
--- a/modules/genai-ecosystem/pages/llm-graph-builder.adoc
+++ b/modules/genai-ecosystem/pages/llm-graph-builder.adoc
@@ -31,6 +31,8 @@ Afterwards you can use different RAG approaches (GraphRAG, Vector, Text2Cypher)
 The front-end is a React Application and the back-end a Python FastAPI application running on Google Cloud Run, but you can deploy it locally using docker compose.
 It uses the https://python.langchain.com/docs/use_cases/graph/constructing[llm-graph-transformer module^] that Neo4j contributed to LangChain and other langchain integrations (e.g. for GraphRAG search).
 
+All Features are documented in detail here: xref::llm-graph-builder-features.adoc[]
+
 Here is a quick demo:
 
 ++++
@@ -93,6 +95,8 @@ You can also run it locally, by cloning the https://github.com/neo4j-labs/llm-gr
 
 It is using Docker for packaging front-end and back-end, and you can run `docker-compose up` to start the whole application.
 
+Local deployment and configuration details are available in the xref::llm-graph-builder-deployment.adoc[]
+
 
 == Videos & Tutorials