The problem
Models are sensitive to prompt evidence. When the retrieval layer mixes live truth with stale or unauthorized material, the generated answer may look polished while being grounded in bad context.
Symptoms
Signals that the issue is happening in production, not just in a benchmark.
The prompt contains multiple versions of the same policy with no freshness signal.
Retrieved chunks are relevant to the words in the query but not the user, tenant, role, or current task.
Memory summaries override live product or account state.
Debugging focuses on the model even though the context was contaminated before generation.
How KyroDB solves
KyroDB solves this at the runtime boundary before prompt assembly.
KyroDB filters and explains omissions before prompt assembly.
ScopeFingerprint makes tenant, namespace, authorization, filters, embedding model, and prompt version part of the context boundary.
Warnings and status let the application avoid treating partial or degraded context as complete truth.
Trace diagnosis separates pollution, freshness, and relevance causes.
Implementation
Practical steps for teams already using an agent backend, vector store, or RAG pipeline.
- 01
Do not send raw top-k chunks directly to the model for high-risk workflows.
- 02
Make scope, authorization, filters, and source freshness part of every retrieval request.
- 03
Inspect omissions and warnings when building prompt context.
- 04
Use feedback and diagnosis to identify repeated pollution sources.
When not to use it
If the model is not grounded in retrieved or remembered context, context pollution is probably not the main failure mode.
Is context pollution the same as hallucination?
No. Context pollution is bad evidence entering the prompt. Hallucination is the model output failure that may result from bad context, missing context, or model behavior.
Can deduplication solve context pollution?
Deduplication helps, but pollution also comes from stale, wrong-scope, unauthorized, irrelevant, or conflicting material.