Documents must be prepared for RAG. What do you produce?
Why D is the answer
Clean chunks with embeddings
Preparing documents for RAG means producing clean chunks with embeddings for retrieval.
In detail
- RAG needs clean chunks with embeddings.
- Keep metadata with each chunk.
Use case
An illustrative scenario, not a real incident.
A pipeline cleans manuals, splits them and generates embeddings, ready for retrieval.
See it in different ways
mindmap
root((Processing))
Dataflow
Apache Beam
Dataproc
Existing Spark
Data Fusion
Visual, code free
Dataform
SQL in BigQuery
Managed Airflow
DAG orchestrationflowchart LR S0["Clean"] --> S1["Chunk"] --> S2["Embed"] --> S3["Store"]
Key point: RAG needs clean chunks with embeddings.
Why the other options are wrong
- A. A larger reservation
- A reservation buys capacity.
- B. A new DAG only
- A DAG alone produces nothing to search.
- C. A longer retention policy
- Retention is unrelated.