diff --git a/content/en/llm_observability/monitoring/patterns.md b/content/en/llm_observability/monitoring/patterns.md
index 84428c9c57f..c1ddf1e8543 100644
--- a/content/en/llm_observability/monitoring/patterns.md
+++ b/content/en/llm_observability/monitoring/patterns.md
@@ -54,6 +54,7 @@ Each topic shows its interaction volume and share of total traffic. Interactions
    - **Time window:** The lookback period for interactions to analyze
    - **Which spans do you want to cluster?:** Filter by application, environment, span type, or other tags to scope the Pattern to a specific slice of traffic.
    - **Sampling Rate:** The percentage of matching interactions to include. Patterns processes up to 10,000 records per run; if your filter matches more than that, records are randomly sampled down to the cap.
+   - **Coverage datasets (optional):** Select one or more datasets from a project to measure offline coverage. When configured, each topic in the run will include a coverage breakdown showing: how many of the topic's production interactions are already covered by records in the selected dataset(s), and how many suggested datapoints are available to improve coverage.
 5. Under **What should we detect Patterns on?**, enter a template that defines what gets sent to the model for analysis. Use {{variable}} syntax to reference any span field — for example, {{meta.input.value}} to analyze patterns by user input, or {{meta.span.kind}} to analyze by span kind. Click {{< ui >}}Template Examples{{< /ui >}} to see common configurations. As you type, the right panel previews matching spans and shows what percentage of interactions have values for the variables you've referenced.
 6. Click **Save**
 
@@ -85,6 +86,7 @@ The topic table provides a hierarchical view of all discovered topics. Each topi
 - {{< ui >}}Errors{{< /ui >}} — error count and rate
 - {{< ui >}}Latency{{< /ui >}} — median latency for interactions in this topic
 - {{< ui >}}Online Evals{{< /ui >}} — evaluation results if online evaluations are configured
+- {{< ui >}}Coverage{{< /ui >}} — when a coverage dataset is configured, shows the ratio of interactions already covered by the dataset and the number of suggested datapoints
  
 
 Expand parent topics to see their sub-topics and examine specific areas of your application's traffic.
@@ -103,6 +105,8 @@ From the interactions table inside a topic's detail view, you can act on the int
 - **Add to Dataset:** Send the interactions to a [Dataset][2] to build evaluation test cases from real production traffic.
 - **Add to Queue:** Send the interactions to an [Annotation Queue][3] for human review and labeling.
 
+When coverage datasets are configured, Patterns marks individual interactions as **suggested** based on which ones would most improve your dataset coverage for each topic. Suggested interactions are highlighted in the interactions table and can be added to your dataset with **Add to Dataset**.
+
 ## Trigger a new run
 
 To analyze your production traffic, click {{< ui >}}Run analysis{{< /ui >}} in the Patterns header. The pipeline runs in the background and takes 5 to 10 minutes. You can close the page and return later — the header shows the last run date and lookback period when the run completes.
@@ -121,6 +125,8 @@ Use traffic percentage to identify your most common use cases. The parent-child
 
 Compare your topic distribution against what your golden datasets actually cover. Look at topics that represent high production volume but have no corresponding evaluation cases: this is where your test coverage has gaps, and where model regressions are least likely to be caught before they reach users.
 
+When a coverage dataset is configured, Patterns computes offline coverage automatically for each topic: each topic shows a global coverage ratio (how many of its production interactions match a record in the dataset) and a per-dataset breakdown. Topics with low coverage and available suggested datapoints are flagged so you can add the most impactful real-world examples to your evaluation datasets.
+
 ### Diagnose failure patterns
 
 Scope your Pattern's filter to spans with poor quality scores or failed evaluations, then run the analysis. The resulting topic taxonomy shows which types of requests are failing most, giving you a structured way to prioritize fixes instead of debugging trace by trace.
diff --git a/content/en/llm_observability/playground.md b/content/en/llm_observability/playground.md
index 66d255142a6..9fd43259119 100644
--- a/content/en/llm_observability/playground.md
+++ b/content/en/llm_observability/playground.md
@@ -133,6 +133,8 @@ In the dialog:
 
 The experiment runs across all records in the dataset—not only the 20-record preview sample. When complete, view results in [{{< ui >}}AI Observability{{< /ui >}} > {{< ui >}}Experiments{{< /ui >}}][2].
 
+<div class="alert alert-info">Playground experiments process up to 10,000 dataset records per run. If your dataset exceeds this limit, only the first 10,000 records are processed.</div>
+
 ## Further reading
 
 {{< partial name="whats-next/whats-next.html" >}}