diff --git a/docs/run/run-jobs-batch.mdx b/docs/run/run-jobs-batch.mdx
index 15de86b4031..cc4d9b38e92 100644
--- a/docs/run/run-jobs-batch.mdx
+++ b/docs/run/run-jobs-batch.mdx
@@ -20,10 +20,18 @@ Batch mode can shorten processing time if all jobs can be provided at the outset
 The following example shows how you can divide up a long list of circuits into multiple jobs and run them as a batch to take advantage of the parallel processing.
 
 ```python
+from qiskit_ibm_runtime import SamplerV2 as Sampler, Batch
+
+max_circuits = 100
+all_partitioned_circuits = []
+for i in range(0, len(circuits), max_circuits):
+    all_partitioned_circuits.append(circuits[i : i + max_circuits])
 jobs = []
-with Batch(backend) as batch:
-    estimator = Estimator(batch)
-    # calls within this context are part of the batch.
-    for obs_set in observable_sets:
-        jobs.append(estimator.run(circuits, observables=obs_set))
+start_idx = 0
+
+with Batch(backend=backend):
+    sampler = Sampler()    
+    for partitioned_circuits in all_partitioned_circuits:
+        job = sampler.run(partitioned_circuits)
+        jobs.append(job)
 ```