Update tidb-cloud/v8.5-performance-highlights.md

tidb-cloud/v8.5-performance-highlights.md

@@ -161,7 +161,7 @@ In the following table of test results, the **Current** column shows the results
 
 Due to the inherent risk of physical disk damage, the cloud disk jitter issue is unavoidable. To mitigate its impact, TiKV introduces a [slow node detection mechanism](https://docs.pingcap.com/tidb/v8.5/pd-scheduling-best-practices#troubleshoot-tikv-node). This mechanism uses [evict-slow-store-scheduler](https://docs.pingcap.com/tidb/v8.5/pd-control#scheduler-show--add--remove--pause--resume--config--describe) to detect and manage slow nodes, reducing the effects of cloud disk jitter.
 
-The severity of disk jitter might also be highly related to users' workload profiles. In latency-sensitive scenarios, designing applications in conjunction with TiDB  features can further minimize the impact of IO jitter on applications. For example, in read-heavy and latency-sensitive environments, adjusting the  [tikv_client_read_timeout](/system-variables.md#tikv_client_read_timeout-new-in-v740) system variable according to latency requirements and using stale reads or follower reads can enable faster failover retries to other replica peers for KV requests sent from TiDB. This reduces the impact of IO jitter on a single TiKV node and helps improve query latency. Note that the effectiveness of this feature depends on the workload profile, which should be evaluated before implementation.
+The severity of disk jitter might also be highly related to users' workload profiles. In latency-sensitive scenarios, designing applications in conjunction with TiDB features can further minimize the impact of IO jitter on applications. For example, in read-heavy and latency-sensitive environments, adjusting the [`tikv_client_read_timeout`](/system-variables.md#tikv_client_read_timeout-new-in-v740) system variable according to latency requirements and using stale reads or follower reads can enable faster failover retries to other replica peers for KV requests sent from TiDB. This reduces the impact of IO jitter on a single TiKV node and helps improve query latency. Note that the effectiveness of this feature depends on the workload profile, which should be evaluated before implementation.
 
 Additionally, cloud users can reduce the probability of jitter by choosing cloud disks with higher performance.