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Make small chart changes without a full AI run

João Viana explains how Lightdash uses Jev to interpret small chart changes, checks the choices, and sends requests that do not pass those checks back to its normal AI assistant.

Original by João VianaAgent workflows

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Credits

“Building a fast path for Lightdash Agents with Jev” by João Viana. Read the original source.

This expanded guide is an AI-narrated adaptation prepared by JevMade. It expands the source’s essential ideas, examples and caveats in JevMade’s own words and is not a word-for-word reading. The synthetic voice does not imitate the author or imply their endorsement.

Our summary

Changing an existing chart can still make an AI assistant repeat a slow planning process. João Viana describes Lightdash’s Fast mode, which uses Jev to identify small follow-up edits. The aim is to change what is already there without starting another full assistant run.

Jev chooses an edit type and the needed fields or values from known options. Lightdash’s software prepares the change, checks confidence levels, and asks Jev whether the plan covers the whole request. If these checks fail, the normal assistant takes over; otherwise, the software applies the edit.

Lightdash reports median replies of 1.7 seconds instead of 17.5 seconds across 25 scripted follow-ups in four conversations. That small comparison does not establish general accuracy. Testing also found missed filter values and broken handoffs, showing why teams need to check the real user route and fallback behavior.

Key takeaways

  1. Choose from chart fields, values and edit types already available; ordinary software applies the selected change.
  2. Check both the confidence of each choice and whether the planned edit covers the whole request before using the shortcut.
  3. Test the route customers actually use and track how often the shortcut runs, not just whether it reports errors.

Lightdash reports a 1.7-second median with Fast mode versus 17.5 seconds for the normal agent across 25 scripted follow-ups in four conversations on its own analytics instance. A separate six-follow-up demonstration used a different model. These are not broad accuracy benchmarks.

Lightdash · Original published

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