diff --git a/content/en/llm_observability/experiments/advanced_runs.md b/content/en/llm_observability/experiments/advanced_runs.md
index fe4e91df67e..db8b6db2671 100644
--- a/content/en/llm_observability/experiments/advanced_runs.md
+++ b/content/en/llm_observability/experiments/advanced_runs.md
@@ -1,10 +1,22 @@
 ---
 title: Advanced Experiment Runs
-description: Run experiments multiple times to account for model variability and automate experiment execution in CI/CD pipelines. 
+description: Run experiments multiple times to account for model variability on a subset of the dataset, and automate experiment execution in CI/CD pipelines. 
 ---
 
 This page discusses advanced topics in running experiments, including [multiple experiment runs](#multiple-runs) and [setting up experiments in CI/CD](#setting-up-your-experiment-in-cicd).
 
+## Run an Experiment on a subset of the Dataset
+
+First, add tags to your dataset records. They can be a unique identifier (e.g `name:test_use_case_1`) or represent a property of the scenario (e.g `difficulty:hard`).
+
+Then, use the `tags` argument of `LLMObs.pull_dataset()` to filter down the dataset to the records you want to run an Experiment on.
+
+Example
+```
+LLMObs.pull_dataset(dataset_name="my-dataset", tags=["env:prod", "version:1.0"])
+```
+Finally, run the Experiment as usual.
+
 ## Multiple runs
 
 You can run the same experiment multiple times to account for model non-determinism. You can use the [LLM Observability Python SDK][1] or [Experiments API][2] to specify how many iterations to run; subsequently, each dataset record is executed that many times using the same tasks and evaluators. 
@@ -215,4 +227,4 @@ jobs:
 
 [1]: /llm_observability/instrumentation/sdk?tab=python
 [2]: /llm_observability/experiments/api
-[3]: https://app.datadoghq.com/llm/experiments
\ No newline at end of file
+[3]: https://app.datadoghq.com/llm/experiments