Static RAG works — until your data gets outdated.

Most RAG pipelines rely on embeddings + a fixed dataset.
That’s fine for internal docs. Not fine for anything that changes daily.

The fix is simple:

👉 add a live search layer before generation

In this setup:

• Python script → sends query to SERP API
• gets fresh results
• turns snippets into structured context
• feeds it into the LLM

Now your RAG isn’t just relevant — it’s current.
You don’t need a full pipeline to start.

Even a small search → context → prompt loop already makes a big difference.

https://levelup.gitconnected.com/how-to-build-a-real-time-data-pipeline-for-rag-in-python-using-bright-datas-serp-api-dc70b32ded37