# Set up the dbt Semantic Layer

## Getting started

There are two options for developing a dbt project, including the Semantic Layer:

* [dbt CLI](../../docs/platform/dbt-cli-installation.md) — MetricFlow commands are embedded in the dbt CLI under the `dbt sl` subcommand. This is the easiest, most full-featured way to develop Semantic Layer code for the time being. You can use the editor of your choice and run commands from the terminal.

* [Studio IDE](../../docs/platform/studio-ide/develop-in-studio.md) — You can create semantic models and metrics in the Studio IDE.

## Basic commands

* 🔍 A less common command that will come in handy with the Semantic Layer is `dbt parse`. This will parse your project and generate a **semantic manifest**, a representation of meaningful connections described by your project. This is uploaded to dbt, and used for running `dbt sl` commands in development. This file gives MetricFlow a **state of the world from which to generate queries**.
* 🧰 `dbt sl query` is your other best friend, it will execute a query against your semantic layer and return a sample of the results. This is great for testing your semantic models and metrics as you build them. For example, if you're building a revenue model you can run `dbt sl query --metrics revenue --group-by metric_time__month` to validate that monthly revenue is calculating correctly.
* 📝 Lastly, `dbt sl list dimensions --metrics [metric name]` will list all the dimensions available for a given metric. This is useful for checking that you're increasing dimensionality as you progress. You can `dbt sl list` other aspects of your Semantic Layer as well, run `dbt sl list --help` for the full list of options.

For more information on the available commands, refer to the [MetricFlow commands](../../docs/build/metricflow-commands.md) reference, or use `dbt sl --help` and `dbt sl [subcommand] --help` on the command line. If you need to set up a dbt project first, check out the [quickstart guides](../../docs/get-started-dbt.md).

## Onward!

Throughout the rest of the guide, we'll show example code based on the Jaffle Shop project, a fictional chain of restaurants. You can check out the code yourself and try things out in the [Jaffle Shop repository](https://github.com/dbt-labs/jaffle-shop). So if you see us calculating metrics like `food_revenue` later in this guide, this is why!

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