RAG Finds Text. TRACE Keeps Receipts: A Trust Layer for High-Stakes Retrieval
Details
Most retrieval systems answer one question well:
“Which text is semantically close to the user’s query?”
In high-stakes domains, that is not enough.
If the corpus contains medical guidance, legal rules, emergency procedures, or operational documentation, a system also needs to reason about trust: who published the source, whether it is current, whether stronger material contradicts it, and whether it has been superseded.
This talk explores where standard RAG pipelines break down and introduces TRACE (Trust-Ranked Adjudicated Corpus Engine), an open approach to making retrieval systems more auditable and reliable.
We will cover:
source authority vs content confidence
chunk-level and claim-span trust
contradiction and supersession handling
append-only adjudication evidence
what users should see at runtime
where this matters for government, field operations, and responsible AI
The goal is not to build another chatbot.
The goal is a system that can explain why it trusted, rejected, or could not rely on a piece of information.
No vendor pitch. Practical focus on failure modes and design trade-offs.
Reference:
https://gist.github.com/Co0olCat/07c23ed35b1686e926131b957331dbea