Context Engineering for AI Agents: Why Fresh Data Beats Better Models

Traditional RAG (Retrieval-Augmented Generation) systems use batch-refreshed vector stores — embeddings computed hours ago from documents that may have changed. Real-time RAG uses streaming materialized views to keep context current, combining fresh structured data with vector similarity search.

Traditional RAG vs Real-Time RAG

Aspect	Traditional RAG	Real-Time RAG
Data freshness	Hours (batch embedding)	Sub-second
Data type	Unstructured documents	Structured + unstructured
Update trigger	Scheduled batch job	Continuous (streaming)
Context quality	Stale when source changes	Always current

Real-Time RAG with RisingWave

RisingWave combines streaming SQL with vector search (v2.6+):

-- Streaming source of support documentation updates
CREATE SOURCE doc_updates (doc_id INT, title VARCHAR, content VARCHAR,
  embedding VECTOR(1536), updated_at TIMESTAMP)
WITH (connector='kafka', topic='doc-updates', ...);

-- Always-current document index
CREATE MATERIALIZED VIEW current_docs AS
SELECT DISTINCT ON (doc_id) doc_id, title, content, embedding, updated_at
FROM doc_updates ORDER BY doc_id, updated_at DESC;

When an agent needs context, it queries the streaming materialized view — which always reflects the latest document versions.

Frequently Asked Questions

What is real-time RAG?

Real-time RAG combines streaming data processing with retrieval-augmented generation. Instead of batch-refreshing a vector store, real-time RAG keeps the retrieval index continuously updated as source data changes.

Does RisingWave support vector search?

Yes. RisingWave v2.6 added vector data type and similarity search, enabling real-time RAG directly within the streaming database.

Building RAG Systems with Real-Time Data