mirrors/n8n-docs
2
0
Fork 0
mirror of https://github.com/n8n-io/n8n-docs.git synced 2025-11-20 17:48:34 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

Fix broken links to headers in release notes (#3279)

Browse Source
This commit is contained in:
Justin Ellingwood 2025-06-18 11:02:16 +01:00 committed by GitHub
parent 8e94a67d4a
commit 2483437e9d
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 2 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
docs/release-notes.md
View File

@ -261,12 +261,12 @@ Get started by selecting an AI workflow you want to evaluate that includes one o
1. Add an **Evaluation** node with the **On new Evaluation event** operation. This node will act as an additional trigger you’ll run only when testing. Configure it to read your dataset from Google Sheets, with each row representing a test input.<br>
> 💡 Better datasets mean better evaluations. Craft your dataset from a variety of test cases, including edge cases and typical inputs, to get meaningful feedback on how your AI performs. Learn more and access sample datasets [here](/advanced-ai/evaluations/light-evaluations.md/#1-create-a-dataset.md).
> 💡 Better datasets mean better evaluations. Craft your dataset from a variety of test cases, including edge cases and typical inputs, to get meaningful feedback on how your AI performs. Learn more and access sample datasets [here](/advanced-ai/evaluations/light-evaluations.md/#1-create-a-dataset).
2. Add a second **Evaluation** node using the **Set Outputs** operation after the part of the workflow you're testing—typically after an LLM or Agent node. This captures the response and writes it back to your dataset in Google Sheets.
3. To evaluate output quality, add a third **Evaluation** node with the **Set Metrics** operation at a point after you’ve generated the outputs. You can develop workflow logic, custom calculations, or add an LLM-as-Judge to score the outputs. Map these metrics to your dataset in the node’s parameters. <br>
> 💡 Well-defined metrics = smarter decisions. Scoring your outputs based on similarity, correctness, or categorization can help you track whether changes are actually improving performance. Learn more and get links to example templates [here](/advanced-ai/evaluations/metric-based-evaluations.md/#2-calculate-metrics.md).
> 💡 Well-defined metrics = smarter decisions. Scoring your outputs based on similarity, correctness, or categorization can help you track whether changes are actually improving performance. Learn more and get links to example templates [here](/advanced-ai/evaluations/metric-based-evaluations.md/#2-calculate-metrics).
<br>