docs: clean up prose flagged by the shared Vale rules (#4596)

The existing docs have 254 findings from our shared Vale rules.
Contributors would otherwise inherit these when editing the same lines.

Clean up the prose across 80 pages: correct product names and a repeated
word, simplify a few phrases, and replace dashes with punctuation that
fits each sentence. The pinned Vale scan now reports zero findings, with
no suppressions.

docs/web/tutorials/agents/agentic-rag.mdx

@@ -4,8 +4,8 @@ sidebarTitle: "Agentic RAG"
 description: "Several agents collaborating to retrieve and answer."
 ---
 
-Builds a RAG system where multiple agents collaborate -- retrieving
-information, checking it, and generating answers -- rather than a single
+Builds a RAG system where multiple agents collaborate (retrieving
+information, checking it, and generating answers) rather than a single
 retrieve-then-generate pass.
 
 <Note>

docs/web/tutorials/agents/contextual-rag.mdx

@@ -6,7 +6,7 @@ description: "Prepend LLM-generated context to each chunk before embedding it."
 
 Addresses the "lost in the middle" problem in retrieval. An LLM generates a
 succinct piece of context for each document chunk, which is prepended to the
-chunk before embedding -- so a chunk carries enough surrounding meaning to be
+chunk before embedding, so a chunk carries enough surrounding meaning to be
 retrieved for the right queries.
 
 <Note>

docs/web/tutorials/agents/hyde.mdx

@@ -4,8 +4,8 @@ sidebarTitle: "HyDE"
 description: "Generate a hypothetical answer, then retrieve documents similar to it."
 ---
 
-An LLM writes a hypothetical document answering the query, and that document --
-not the query -- is embedded and used for retrieval. It works because a
+An LLM writes a hypothetical document answering the query, and that document,
+not the query, is embedded and used for retrieval. It works because a
 plausible answer sits nearer the real answers in embedding space than a question
 does.
 

docs/web/tutorials/agents/matryoshka-embeddings.mdx

@@ -5,7 +5,7 @@ description: "Nested, variable-size embeddings with LanceDB and LlamaIndex."
 ---
 
 A RAG pipeline built on Matryoshka embeddings with LlamaIndex. The embeddings
-nest, so one model serves several dimensionalities -- store the full vector and
+nest, so one model serves several dimensionalities: store the full vector and
 retrieve with a truncated one when the cheaper comparison is good enough.
 
 <Note>

docs/web/tutorials/agents/nvidia-rag-blueprint/index.mdx

@@ -150,7 +150,7 @@ This example is meant to prove more than a trivial vector lookup.
 - the adapter supports `RRFReranker`, `MRRReranker`, and `CrossEncoderReranker`
 - the default example uses `MRRReranker`, not a plain weighted linear combination
 
-That matters for NVIDIA partner workloads because product names, storage platforms, and technical
+Hybrid retrieval is important for NVIDIA partner workloads because product names, storage platforms, and technical
 jargon often need exact lexical matching as well as semantic retrieval.
 
 ## How this can be extended {#how-this-can-be-extended}

docs/web/tutorials/agents/time-travel-rag/index.mdx

@@ -11,7 +11,7 @@ All the scripts and code for this tutorial are available in the
 
 ## Use case: Financial services regulatory knowledge base {#use-case-financial-services-regulatory-knowledge-base}
 
-Imagine you're a major investment bank. Your team is tasked with building a critical Retrieval-Augmented Generation (RAG) system. This system must provide instant, accurate answers to compliance officers about ever-changing financial regulations. A wrong or out-of-date answer isn't just an inconvenience—it could lead to multi-million dollar fines, reputational damage, and regulatory audits.
+Imagine you're a major investment bank. Your team is tasked with building a critical Retrieval-Augmented Generation (RAG) system. This system must provide instant, accurate answers to compliance officers about ever-changing financial regulations. A wrong or out-of-date answer isn't just an inconvenience: it could lead to multi-million dollar fines, reputational damage, and regulatory audits.
 
 Your knowledge base is a living entity, constantly evolving with: