cosmetic grammar maven cleanups to markdown document (#7021)

docs/getting_started/concepts.md

@@ -32,9 +32,7 @@ A data connector (i.e. `Reader`) ingest data from different data sources and dat
 [**Documents / Nodes**](/core_modules/data_modules/documents_and_nodes/root.md): A `Document` is a generic container around any data source - for instance, a PDF, an API output, or retrieved data from a database. A `Node` is the atomic unit of data in LlamaIndex and represents a "chunk" of a source `Document`. It's a rich representation that includes metadata and relationships (to other nodes) to enable accurate and expressive retrieval operations.
 
 [**Data Indexes**](/core_modules/data_modules/index/root.md): 
-Once you've ingested your data, LlamaIndex help you index data into a format that's easy to retrieve.
-Under the hood, LlamaIndex parse the raw documents into intermediate representations, calculate vector embeddings, and infer metadata, etc.
-The most commonly used index is the [VectorStoreIndex](/core_modules/data_modules/index/vector_store_guide.ipynb)
+Once you've ingested your data, LlamaIndex will help you index the data into a format that's easy to retrieve. Under the hood, LlamaIndex parses the raw documents into intermediate representations, calculates vector embeddings, and infers metadata. The most commonly used index is the [VectorStoreIndex](/core_modules/data_modules/index/vector_store_guide.ipynb)
 
 ### Querying Stage
 In the querying stage, the RAG pipeline retrieves the most relevant context given a user query,
@@ -51,7 +49,7 @@ These building blocks can be customized to reflect ranking preferences, as well
 #### Building Blocks
 [**Retrievers**](/core_modules/query_modules/retriever/root.md): 
 A retriever defines how to efficiently retrieve relevant context from a knowledge base (i.e. index) when given a query.
-The specific retrieval logic differs for difference indices, the most popular being dense retrieval against a vector index.
+The specific retrieval logic differs for different indices, the most popular being dense retrieval against a vector index.
 
 [**Node Postprocessors**](/core_modules/query_modules/node_postprocessors/root.md):
 A node postprocessor takes in a set of nodes, then apply transformation, filtering, or re-ranking logic to them. 
@@ -80,4 +78,4 @@ This gives it additional flexibility to tackle more complex tasks.
 * tell me how to [customize things](/getting_started/customization.rst).
 * curious about a specific module? Check out the module guides 👈
 * have a use case in mind? Check out the [end-to-end tutorials](/end_to_end_tutorials/use_cases.md)
-```
\ No newline at end of file
+```