Rephrase sentences

Signed-off-by: Naveen Tatikonda <[email protected]>

_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md

@@ -20,7 +20,7 @@ leading to higher memory requirements and increased operational costs. Faiss sca
 
 ## Why use Faiss scalar quantization?
 
-When you index vectors in [OpenSearch 2.13](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.13.0.md) or later versions, you can configure your k-NN index to apply _scalar quantization_. Scalar quantization converts each dimension of a vector from a 32-bit floating-point (`fp32`) to a 16-bit floating-point (`fp16`) representation. Using the Faiss scalar quantizer (SQfp16), integrated in the k-NN plugin, you can get up to a 50% memory savings with a very minimal loss of recall (see [Benchmarking results](#benchmarking-results)). When used with [SIMD optimization](https://opensearch.org/docs/latest/search-plugins/knn/knn-index#simd-optimization-for-the-faiss-engine), 
+When you index vectors in [OpenSearch 2.13](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.13.0.md) or later versions, you can configure your k-NN index to apply _scalar quantization_. Scalar quantization converts each dimension of a vector from a 32-bit floating-point (`fp32`) to a 16-bit floating-point (`fp16`) representation. Using the Faiss scalar quantizer (SQfp16), integrated in the k-NN plugin, saves about 50% of the memory with minimal reduction in recall (see [Benchmarking results](#benchmarking-results)). When used with [SIMD optimization](https://opensearch.org/docs/latest/search-plugins/knn/knn-index#simd-optimization-for-the-faiss-engine), 
 SQfp16 quantization can also significantly reduce search latencies and improve indexing throughput.
 
 ## How to use Faiss scalar quantization
@@ -234,8 +234,7 @@ To achieve even greater memory efficiency, we plan to introduce `int8` quantizat
 This technique will enable a remarkable 75% reduction in memory requirements, or 4x compression, compared to full-precision vectors and we expect to find minimal reduction in recall. 
 The quantizers will accept `fp32` vectors as input, perform online training, and quantize the data into byte-sized vectors, eliminating the need for external quantization or extra training steps.
 
-Furthermore, we aim to release binary vector support, enabling an unprecedented 32x compression rate. This approach will further reduce memory consumption. In 
-addition to this we will soon add support for avx512 optimization which helps to further reduce search latency.
+Furthermore, we aim to release binary vector support, enabling an unprecedented 32x compression rate. This approach will further reduce memory consumption. Moreover, we plan to incorporate AVX-512 optimization, which will contribute to further reducing search latency.
 
 Our ongoing analysis and tuning of OpenSearch lets you address large-scale similarity search while minimizing resource requirements and maximizing cost-effectiveness.