Apply suggestions from code review

Signed-off-by: kolchfa-aws <[email protected]>

Author: kolchfa-aws

_posts/2025-05-08-opensearch-performance-3.0.md

@@ -21,9 +21,9 @@ featured_image: false
 #additional_author_info: We sincerely appreciate the contributions to this blog from Anandhi Bumstead, Carl Meadows, Jon Handler, Dagney Braun, Michael Froh, Kunal Khatua, Andrew Ross, Harsha Vamsi, Bowen Lan, Rishabh Kumar Maurya, Sandesh Kumar, Marc Handalian, Rishabh Singh, Govind Kamat, Martin Gaievski, and Minal Shah.
 ---
 
-OpenSearch 3.0 marks a major milestone in the project's ongoing performance journey and the first major release since 2.0 in April 2022. Building on the enhancements of the 2.x series, the 3.0 release integrates **Apache Lucene 10** and upgrades the Java runtime to **JDK 21**, bringing significant improvements to search throughput, indexing and query latency, and vector processing. With a **10x search performance boost** and **2.5x vector search performance boost**, Lucene 10 continues to be our strategic search library, and its latest version delivers measurable gains over earlier releases (baselined against 1.x) through enhanced query execution, skip-based filtering, and segment-level concurrency. 
+[OpenSearch 3.0](https://opensearch.org/blog/unveiling-opensearch-3-0/) marks a major milestone in the project's ongoing performance journey and the first major release since 2.0 in April 2022. Building on the enhancements of the 2.x series, the 3.0 release integrates **Apache Lucene 10** and upgrades the Java runtime to **JDK 21**, bringing significant improvements to search throughput, indexing and query latency, and vector processing. With a **10x search performance boost** and **2.5x vector search performance boost**, Lucene 10 continues to be our strategic search library, and its latest version delivers measurable gains over earlier releases (baselined against 1.x) through enhanced query execution, skip-based filtering, and segment-level concurrency. 
 
-This post provides a detailed update on OpenSearch 3.0's performance, focusing on search queries, indexing throughput, artificial intelligence and machine learning (AI/ML) use cases, and vector search workloads. We'll highlight measurable impacts, as observed in our benchmarks, and explain how new Lucene 10 features, such as concurrent segment search, query optimizations, doc-value skip lists, and prefetch APIs, contribute to future OpenSearch roadmaps. All results are supported by benchmark data, in keeping with our focus on community transparency and real-world impact.
+This post provides a detailed update on OpenSearch 3.0's performance, focusing on search queries, indexing throughput, artificial intelligence and machine learning (AI/ML) use cases, and vector search workloads. We'll highlight measurable impacts, as observed in our benchmarks, and explain how new Lucene 10 features, such as concurrent segment search, query optimizations, doc-value skip lists, and prefetch APIs, contribute to future [OpenSearch roadmaps](https://github.com/orgs/opensearch-project/projects/206). All results are supported by benchmark data, in keeping with our focus on community transparency and real-world impact.
 
 ## Query performance improvements in OpenSearch 3.0