Update src/content/docs/workers-ai/tutorials/using-bigquery-with-workers-ai.mdx

Co-authored-by: Jun Lee <[email protected]>

Author: daisyfaithauma

src/content/docs/workers-ai/tutorials/using-bigquery-with-workers-ai.mdx

@@ -677,7 +677,7 @@ Once you obtained the results, you formatted them to later be passed to generati
 
 ## Next Steps
 
-If instead of displaying the results of ingesting the data to the AI model in a browser, your workflow requires fetching and store data (for example in [R2](/r2/) or [D1](/d1/)) on regular intervals, you may want to consider adding a [scheduled handler](/workers/runtime-apis/handlers/scheduled/) for this Worker. It allows triggering the Worker with a predefined cadence via a [Cron Trigger](/workers/configuration/cron-triggers/).
+If, instead of displaying the results of ingesting the data to the AI model in a browser, your workflow requires fetching and store data (for example in [R2](/r2/) or [D1](/d1/)) on regular intervals, you may want to consider adding a [scheduled handler](/workers/runtime-apis/handlers/scheduled/) for this Worker. It allows triggering the Worker with a predefined cadence via a [Cron Trigger](/workers/configuration/cron-triggers/).
 
 A use case to ingest data from other sources, like you did in this tutorial, is to create a RAG system. If this sounds relevant to you, please check out the tutorial [Build a Retrieval Augmented Generation (RAG) AI](/workers-ai/tutorials/build-a-retrieval-augmented-generation-ai/).