Change Data Capture (CDC) for RAG Systems: How to Keep Your AI Up to Date

Your RAG Isn't Hallucinating — It's Using Outdated Data

Most teams building Retrieval-Augmented Generation (RAG) systems spend their time tuning LLMs, prompts, and vector databases. Meanwhile, the data pipeline is often neglected and updated far less frequently than the product demands.

The result?

• ●Confident-sounding answers
• ●Proper citations
• ●Incorrect or outdated information

This usually isn't a model problem. It's a data freshness problem caused by outdated ingestion strategies.

The Real Issue: Batch Ingestion in a Real-Time World

Many RAG pipelines still rely on:

• ●Nightly batch jobs
• ●Weekly re-indexing
• ●Manual embedding refreshes

This approach creates serious issues:

• ●Important updates don't appear when they matter.
• ●Deleted records continue to exist in vector stores.
• ●Corrections never fully replace old facts

Your AI isn't hallucinating — it's accurately answering questions about data that no longer reflects reality.

What Is Change Data Capture (CDC)?

Change Data Capture (CDC) tracks and streams only the data that has changed in a system.

Instead of reprocessing entire databases, CDC captures events such as:

• ●New records
• ●Updated records
• ●Deleted records

Each change is propagated downstream automatically and incrementally.

In simple terms:

• ●New row? Sync it.
• ●Updated row? Sync it.
• ●Deleted row? Remove it everywhere.

No full-table scans. No wasted compute. No stale data.

Why CDC Is Ideal for RAG Pipelines

RAG systems depend heavily on accurate and current context. CDC aligns perfectly with this requirement.

Key benefits of using CDC for RAG:

• ●Near real-time knowledge base updates
• ●Incremental embedding generation (lower compute and GPU costs)
• ●Reliable delete propagation for compliance and accuracy
• ●Clear, event-driven data lineage

Instead of rebuilding an entire index, your system simply reacts to changes:

Data changes → embeddings update → retriever stays current

Fewer Hallucinations Through Fresher Context

Many so-called hallucinations are actually correct answers to outdated information.

CDC reduces these issues by ensuring:

• ●Retrievers receive the most recent data.
• ●Old facts are replaced, not duplicated.
• ●Models are less likely to guess or fill gaps.

The result is higher trust, better accuracy, and fewer confusing responses.

Turning RAG Into an Event-Driven System

When CDC is integrated into a RAG pipeline, the system becomes reactive instead of static:

• ●Data changes automatically trigger re-embedding
• ●Deleted data is removed from vector stores.
• ●Metadata and permissions remain in sync.
• ●The knowledge base evolves continuously.

Your RAG system starts behaving like real software — not a snapshot frozen in time.

When Batch Ingestion Becomes a Liability

If your RAG system is:

• ●User-facing
• ●Compliance-sensitive
• ●Supporting critical decisions
• ●Expected to be consistently accurate

Then batch ingestion introduces unnecessary risk.

CDC delivers:

• ●Lower latency
• ●Lower infrastructure costs
• ●Better data lineage and auditability
• ●Fewer incidents and debugging cycles

Key Takeaway

If your AI feels unreliable, don't blame the LLM first.

Ask instead:

• ●How fresh is the underlying data?
• ●How quickly do updates propagate?
• ●Do deletions actually delete?

RAG reliability is not primarily a modeling problem. It's a data movement problem — and Change Data Capture is how you keep your AI aligned with the present, not last quarter's reality.