Small updates to docs

README.md

@@ -46,7 +46,7 @@ RAFT relies heavily on RMM which eases the burden of configuring different alloc
 
 ### Multi-dimensional Arrays
 
-The APIs in RAFT currently accept raw pointers to device memory and we are in the process of simplifying the APIs with the [mdspan](https://arxiv.org/abs/2010.06474) multi-dimensional array view for representing data in higher dimensions similar to the `ndarray` in the Numpy Python library. RAFT also contains the corresponding owning `mdarray` structure, which simplifies the allocation and management of multi-dimensional data in both host and device (GPU) memory.
+The APIs in RAFT accept the [mdspan](https://arxiv.org/abs/2010.06474) multi-dimensional array view for representing data in higher dimensions similar to the `ndarray` in the Numpy Python library. RAFT also contains the corresponding owning `mdarray` structure, which simplifies the allocation and management of multi-dimensional data in both host and device (GPU) memory.
 
 The `mdarray` forms a convenience layer over RMM and can be constructed in RAFT using a number of different helper functions:
 

docs/source/index.rst

@@ -1,5 +1,5 @@
-Welcome to RAFT's documentation!
-=================================
+RAPIDS RAFT: Reusable Accelerated Functions and Tools
+=====================================================
 
 RAFT contains fundamental widely-used algorithms and primitives for scientific computing, data science and machine learning. The algorithms are CUDA-accelerated and form building-blocks for rapidly composing analytics.