I audited a clinical RAG pipeline and found it was retrieving a 2023 FDA guideline with 94% cosine similarity — the agent had no idea it was superseded. Here's the decay score that would have caught it.
Been working on the context rot problem in enterprise
RAG for a while. Standard vector retrieval has no
concept of time — it measures semantic similarity,
not temporal relevance.
Ran a test query today: "best practices for deploying
LLM agents in enterprise environments"
Here's what came back:
- A regulatory compliance paper: 265 days old,
decay score 0.395, labeled "aging", 55 days until
it goes stale, penalty multiplier 0.742
- A paper published yesterday: decay score 0.002,
freshness 0.998
- Knowledge velocity: "Field is moving fast.
Refresh your index every 21 days."
Your LLM sees both documents. It cannot tell which
one to trust. Mine can.
The API calculates domain velocity, applies
authority-weighted exponential decay, and returns
a days_until_stale integer on every retrieved chunk
before it hits the LLM. No LLM in the scoring layer.
Pure math. Fully auditable.
Happy to share the endpoint if anyone wants to run
it against their own queries.
What's your current approach to handling temporal
relevance in retrieval? Curious how others are
solving this.