diff --git a/docs/architecture.mdx b/docs/architecture.mdx
new file mode 100644
index 00000000..9d15cffc
--- /dev/null
+++ b/docs/architecture.mdx
@@ -0,0 +1,74 @@
+---
+sidebar_position: 1.9
+sidebar_label: Architecture
+description:
+  "Learn about the high-level architecture of PromptQL and the importance of the semantic metadata layer in giving you
+  reliable and accurate answers to your questions."
+keywords:
+  - promptql
+  - architecture
+---
+
+# Architecture
+
+## Introduction
+
+Below, you'll find a functional overview of PromptQL's architecture and the key components that power it. At the heart
+of PromptQL is the **semantic metadata layer** — the intelligent foundation that makes natural language data queries
+possible.
+
+## High-level overview
+
+```mermaid
+
+flowchart TD
+    Client[Client] --> PlaygroundServer[Playground Server]
+    Metadata[Semantic Metadata] --> PlaygroundServer
+    PlaygroundServer --> LLM[LLM-of-choice]
+    LLM --> QueryPlan[Query Plan]
+    QueryPlan --> PlaygroundServer
+    PlaygroundServer --> Runtime[Python Runtime]
+    Runtime --> Engine[Distributed Query Engine]
+    Engine --> Connectors[Data Connectors]
+    Connectors --> DataSources[Data Sources]
+```
+
+## How it works
+
+### Request initiation
+
+When you ask PromptQL a question, the **Playground Server** receives your natural language query and combines it with
+your **semantic metadata** — the intelligent layer that describes your connected data sources, schemas, relationships,
+business logic, and access controls.
+
+This metadata layer is generated automatically when you connect a data source and evolves as you and your team use
+PromptQL.
+
+### Query plan generation
+
+The Playground Server sends both your question and the semantic metadata to your chosen LLM. **This semantic context is
+what transforms a general-purpose LLM into a data expert for your specific organization.** The metadata tells the LLM
+exactly what data exists, how it's structured, what different fields mean in your business context, and how tables
+relate to each other.
+
+With this rich context, the LLM generates a precise query plan that maps your natural language request to the right data
+operations.
+
+### Query execution
+
+The Playground Server passes the query plan to PromptQL's **Python runtime**, which executes it through the
+**distributed query engine**. The engine uses **data connectors** to fetch data from your **data sources**, respecting
+all access controls and mappings defined in your metadata.
+
+### Response delivery
+
+The retrieved data flows back through the same path: from data sources through connectors, engine, and runtime, where
+it's packaged and returned to you via the Playground Server.
+
+This architecture ensures that your LLM has complete context about your data landscape while maintaining security and
+performance through the distributed execution layer.
+
+## Next steps
+
+With a broad perspective of the different services powering a PromptQL application, we recommend learning more about the
+[semantic metadata layer](/data-modeling/overview.mdx) next.
diff --git a/src/theme/DocSidebar/categories.ts b/src/theme/DocSidebar/categories.ts
index d566c812..b96c7aaf 100644
--- a/src/theme/DocSidebar/categories.ts
+++ b/src/theme/DocSidebar/categories.ts
@@ -12,7 +12,7 @@ export const CATEGORY_CONFIG: Record<string, CategoryConfig> = {
   },
   coreConcepts: {
     title: 'Core Concepts', 
-    directories: ['data-modeling', 'data-sources', 'business-logic', 'auth'],
+    directories: ['architecture', 'data-modeling', 'data-sources', 'business-logic', 'auth'],
     exactMatch: false,
   },
   buildingApps: {