updating name of file

Author: torimwhaley

docs.json

@@ -207,7 +207,7 @@
             "pages": [
               "references/integrations",
               "references/integrations/dbt-projects",
-              "references/integrations/dbt-less",
+              "references/integrations/lightdash-yaml",
               "references/integrations/slack-integration",
               "references/integrations/google-sheets",
               "references/integrations/lightdash-mcp"

guides/lightdash-semantic-layer.mdx

@@ -39,7 +39,7 @@ Tables represent important business objects like customers, orders, or products.
 Lightdash's semantic layer works with your existing data warehouse through four key steps:
 
 1. **Data warehouse connection:** Lightdash connects directly to tables or views in your data warehouse.
-2. **YAML configuration:** You define your semantic layer through YAML configuration files that specify metrics, dimensions, and their relationships. This approach makes definitions transparent, version-controllable, and easy to maintain. You can define your semantic layer within a [dbt project](/references/integrations/dbt-projects) or use [dbt-less Lightdash](/references/integrations/dbt-less) if you don't have dbt.
+2. **YAML configuration:** You define your semantic layer through YAML configuration files that specify metrics, dimensions, and their relationships. This approach makes definitions transparent, version-controllable, and easy to maintain. You can define your semantic layer within a [dbt project](/references/integrations/dbt-projects) or use [Lightdash YAML](/references/integrations/lightdash-yaml) if you don't have dbt.
 3. **Metadata enrichment:** Lightdash lets you add business-friendly labels, descriptions, and formatting to make the data easier to understand. This metadata turns technical fields into business concepts.
 4. **Dynamic query generation:** When users interact with the semantic layer, Lightdash automatically generates optimized SQL queries, handling all the complexity of joins, aggregations, and filters behind the scenes.
 

references/integrations/dbt-less.mdx …ferences/integrations/lightdash-yaml.mdxreferences/integrations/dbt-less.mdx renamed to references/integrations/lightdash-yaml.mdx references/integrations/dbt-less.mdx renamed to references/integrations/lightdash-yaml.mdx

@@ -1,20 +1,20 @@
 ---
-title: "dbt-less Lightdash"
-sidebarTitle: dbt-less
+title: "Lightdash YAML"
+sidebarTitle: Lightdash YAML
 description: "Use Lightdash without a dbt project by defining your semantic layer directly in YAML files."
 ---
 
 <Frame>
   <iframe width="100%" height="420" src="https://www.loom.com/embed/9996f4b7e4574ec098fabb6db824115e" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen />
 </Frame>
 
-## What is dbt-less Lightdash?
+## What is Lightdash YAML?
 
-dbt-less Lightdash allows you to use Lightdash without an existing dbt project. Instead of defining your semantic layer within dbt model YAML files, you define it directly in standalone YAML files that point to tables in your data warehouse.
+Lightdash YAML allows you to use Lightdash without an existing dbt project. Instead of defining your semantic layer within dbt model YAML files, you define it directly in standalone YAML files that point to tables in your data warehouse.
 
 This approach lets you leverage Lightdash's powerful features without needing to adopt dbt first.
 
-## Why use dbt-less Lightdash?
+## Why use Lightdash YAML?
 
 Lightdash has always operated with dbt at its core. Traditionally, customers have active dbt projects, and Lightdash builds its semantic layer within that dbt context.
 
@@ -24,21 +24,21 @@ However, not every team uses dbt. If you're interested in Lightdash features lik
 - **A semantic layer** with consistent metric definitions
 - **Self-service analytics** for your business users
 
-...but you don't have dbt set up, dbt-less Lightdash provides a path forward. You can define your semantic layer directly and start using Lightdash immediately, without the overhead of adopting dbt.
+...but you don't have dbt set up, Lightdash YAML provides a path forward. You can define your semantic layer directly and start using Lightdash immediately, without the overhead of adopting dbt.
 
-## dbt vs dbt-less: which should you use?
+## dbt vs Lightdash YAML: which should you use?
 
 | Scenario | Recommendation |
 |----------|----------------|
 | You already have a dbt project | Use the standard [dbt integration](/references/integrations/dbt-projects) |
 | You're planning to adopt dbt soon | Consider setting up dbt first, then connecting to Lightdash |
-| You don't use dbt and want to try Lightdash quickly | Use dbt-less Lightdash |
-| You want AI agents or semantic layer features without dbt | Use dbt-less Lightdash |
-| You have tables in your warehouse ready to explore | Use dbt-less Lightdash |
+| You don't use dbt and want to try Lightdash quickly | Use Lightdash YAML |
+| You want AI agents or semantic layer features without dbt | Use Lightdash YAML |
+| You have tables in your warehouse ready to explore | Use Lightdash YAML |
 
-The good news: if you start with dbt-less Lightdash and later decide to adopt dbt, the YAML formats are compatible, so migration is straightforward.
+The good news: if you start with Lightdash YAML and later decide to adopt dbt, the YAML formats are compatible, so migration is straightforward.
 
-## Getting started with dbt-less Lightdash
+## Getting started with Lightdash YAML
 
 ### Prerequisites
 
@@ -156,7 +156,7 @@ With access to your warehouse schema, AI copilots can auto-generate dimension an
 
 ## Next steps
 
-Once you've deployed your dbt-less project:
+Once you've deployed your Lightdash YAML project:
 
 - [Explore your data](/get-started/exploring-data/using-explores) in the Lightdash UI
 - [Create metrics](/get-started/develop-in-lightdash/how-to-create-metrics) to define your key business calculations