diff --git a/modules/shared/con-ai-reference-and-tool-calling-capabilities-through.adoc b/modules/shared/con-ai-reference-and-tool-calling-capabilities-through.adoc
index 37ebafa7355..85f37be6a8a 100644
--- a/modules/shared/con-ai-reference-and-tool-calling-capabilities-through.adoc
+++ b/modules/shared/con-ai-reference-and-tool-calling-capabilities-through.adoc
@@ -4,29 +4,24 @@
 = AI reference and tool-calling capabilities through {lcs-name}
 
 [role="_abstract"]
-Review the core components managed by the {lcs-name}({lcs-short}) sidecar container to plan integrations with large language models (LLM) and tool runtime providers.
+You can configure AI integrations through the {lcs-name} ({lcs-short}) sidecar container, which manages all connections between {product-very-short} and your LLM and tool runtime providers.
 
-The {lcs-short} container deploys as a sidecar to extend {product-very-short} functionality. The container integrates and manages the following core architectural components:
+{lcs-short} integrates and manages the following core components:
 
 * Large language model (LLM) inference providers
-
 * Model Context Protocol (MCP) or Retrieval Augmented Generation (RAG) tool runtime providers
 +
 [IMPORTANT]
 ====
 Verify that your model supports tool calling before you enable MCP features. Using an incompatible model results in error messages.
 ====
-
 * Safety providers
-
 * Vector database settings
 
-{lcs-short} also manages critical operational configuration and key data, specifically:
+{lcs-short} also manages the following operational data:
 
 * User feedback collection
-
 * MCP server configuration
-
 * Chat history
 
-{ls-short} sends prompts and receives LLM responses through the {lcs-short} sidecar.
+All prompts and LLM responses pass through the {lcs-short} sidecar.
diff --git a/modules/shared/con-architecture-for-your-ai-backend-deployment.adoc b/modules/shared/con-architecture-for-your-ai-backend-deployment.adoc
index 5272ceafd17..5b9ed223640 100644
--- a/modules/shared/con-architecture-for-your-ai-backend-deployment.adoc
+++ b/modules/shared/con-architecture-for-your-ai-backend-deployment.adoc
@@ -4,9 +4,11 @@
 = {ls-brand-name} architecture for your AI backend deployment
 
 [role="_abstract"]
-Review the {ls-short} component architecture to plan your system layout and coordinate connections with your artificial intelligence (AI) backend deployment.
+You can plan your AI backend deployment by understanding how {ls-short} uses the {lcs-name} ({lcs-short}) container to connect {product-very-short} with your large language model (LLM) providers.
 
-The architecture relies on the {lcs-name} ({lcs-short}) container, which operates as the primary intermediary layer to manage {ls-short} functionality and console user interactions. By default, the interface appears as a floating action button (FAB) on all platforms that host {product-very-short}.
+The {lcs-short} container serves as the sole integration layer between {product-very-short} and your AI backend services. All LLM inference, tool calling, safety, and retrieval-augmented generation (RAG) requests pass through {lcs-short}, which runs as a sidecar container alongside {product-very-short}.
+
+By default, {product-very-short} displays the {ls-short} interface as a floating action button (FAB) on all platforms.
 
 .Additional resources
 * link:https://access.redhat.com/support/policy/updates/developerhub[{product} Life Cycle and supported platforms]