The problem
Hallucination is often blamed on the model even when the prompt contains stale, partial, conflicting, or unauthorized evidence.
Symptoms
Signals that the issue is happening in production, not just in a benchmark.
The model cites retrieved text that was accurate yesterday but wrong today.
Responses blend old and new facts because the prompt contained both.
The RAG stack cannot prove which retrieved item grounded the final answer.
Safety prompts tell the model not to hallucinate, but retrieval still feeds it bad context.
How KyroDB solves
KyroDB solves this at the runtime boundary before prompt assembly.
KyroDB reduces context-caused hallucination by blocking stale or unprovable context in strict mode.
ContextPacket provenance makes the grounding material inspectable.
Warnings, omissions, and packet status let the app change behavior before asking the model to answer.
Replay can compare baseline and candidate retrieval behavior after a hallucination incident.
Implementation
Practical steps for teams already using an agent backend, vector store, or RAG pipeline.
- 01
Gate high-risk RAG responses on ContextPacket status.
- 02
Include provenance and trace id in internal logs for every answer.
- 03
Treat stale-blocked or degraded packets as product states, not hidden errors.
- 04
Use proof reports to show why an answer was grounded or refused.
When not to use it
If hallucinations are caused by pure model reasoning with no retrieved context, KyroDB addresses only part of the problem.
Does KyroDB eliminate hallucinations?
No. KyroDB reduces hallucinations caused by stale, wrong-scope, polluted, or untraceable context before the model generates.
Should I still use citations?
Yes. Citations help users inspect sources. KyroDB adds runtime evidence about freshness, scope, omissions, and traceability behind those sources.