[atravitz]

Description

we've received feedback that users use LLMs to access our docs, and that they generally work well but sometimes do not find information. This seems like a way to make parsing more direct without degrading the human readability.

LLM / AI generated code disclosure

LLMs or other AI-powered tools (beyond simple IDE use cases) were used in this contribution:

• No
• Yes: [If yes, please provide details here]

Checklist

• Read the AI Policy and filled in the AI-generated code disclosure.
• Adhered to the contribution guidelines
• All new code is appropriately documented (user-facing code must have complete docstrings).
• Ran pre-commit: you can run pre-commit locally or comment on this PR with pre-commit.ci autofix.
• Added a news entry, or the changes are not user-facing.

Manual Tests: these are slow so don't need to be run every commit, only before merging and when relevant changes are made (generally at reviewer-discretion).

• GPU integration tests
• example notebook testing
• packaging tests: run this for any large feature PRs or PRs that add test data.

Developers certificate of origin

• I certify that this contribution is covered by the MIT License here and the Developer Certificate of Origin at https://developercertificate.org/.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 90.45%. Comparing base (75562a6) to head (c6216f8).

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #2297      +/-   ##
==========================================
- Coverage   95.14%   90.45%   -4.69%     
==========================================
  Files         227      227              
  Lines       22477    22477              
==========================================
- Hits        21385    20332    -1053     
- Misses       1092     2145    +1053     

Flag	Coverage Δ
fast-tests	90.45% <ø> (?)
slow-tests	?

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