docs: correct data workflows, SQL and operations (#4534)

This PR is part of the documentation revamp. Once the full set of PRs is
merged, we’ll publish the combined site to staging for review before
updating docs.lancedb.com.

The curation, SQL and operations pages contain outdated claims and
examples that do not reliably work as written.

This corrects those pages against the reviewed SDK and local server
evidence, including prepared statements, background jobs, cleanup and
monitoring. Integration guides are grouped with their examples in #4533.

Function and Secret-object documentation stays unpublished until its
release. The two Function pages are listed in `.mintignore` and left out
of the navigation, and the sections about them on other pages are MDX
comments.

Stacked on #4533. Each MDX file has one PR owner. Rebase onto main after
#4533 is squash-merged.

Local assembly, Mint validation and link checks pass, and the assembled
site shows no Function or Secret-object content. A local release
rehearsal that uncomments the drafts and restores the navigation also
passes. Hold publication until this PR and lancedb/sophon#8688 are on
`main`.

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

@@ -1,7 +1,7 @@
 ---
 title: "Time-Travel RAG with versioned data"
 sidebarTitle: "Time-travel RAG"
-description: "Learn how to build production-ready RAG systems with LanceDB's time-travel capabilities for regulatory compliance and audit trails."
+description: "Build a RAG example that retrieves documents from a selected table version."
 ---
 
 <Tip>
@@ -32,7 +32,9 @@ vector databases are ill-equipped to handle.
 
 4. "We need to A/B test a new chunking strategy, but we can't disrupt the production system or duplicate the entire dataset." Experimentation is vital for improvement, but it can't come at the cost of production stability or a massive infrastructure bill.
 
-LanceDB's [zero-cost data evolution](/management/table#schema) and [time-travel capabilities](https://docs.lancedb.com/management/table#versioning) directly address these critical enterprise pain points, providing the foundation for a reliable, auditable, and production-ready RAG system.
+LanceDB's [column-based data evolution](/management/table#schema) and [time-travel capabilities](https://docs.lancedb.com/management/table#versioning) help preserve the dataset used for a particular answer. This tutorial demonstrates
+versioned retrieval; production auditing also needs application logs, retention
+policies and access controls.
 
 ## Dataset: The U.S. Federal Register {#dataset-the-us-federal-register}