Beyond Retrieval
Retrieval finds what's relevant. It doesn't determine what's allowed to be true. As knowledge bases grow, the gap between topical relevance and situational correctness becomes the primary source of failure — and the hardest to detect. This series examines that gap, introduces a framework for closing it, and shows what it takes to build systems that select the right branch of reality before they answer.
Introduction
RAG promised to solve hallucination by grounding AI in trusted sources. In small, stable domains, it delivers. But as knowledge bases grow, a different failure takes over — one that's harder to detect and more expensive to fix.
The system retrieves correct, well-cited information and produces confident answers that don't govern the case at hand. The policy is real but expired. The warranty terms are accurate but belong to a different program. The procedure is valid but assumes a region the customer isn't in. Nothing is fabricated. Everything is cited. The answer is still wrong.
This is the applicability problem: the gap between what's relevant and what's allowed to be true for a given situation. Standard RAG architectures have no representation of this gap, and standard evaluation metrics don't measure it.
This series names the problem, builds a framework for reasoning about it, and works through what it actually takes to make retrieval systems that know which answer governs before they start generating.
Subscribe to Pinecone
Get the latest updates via email when they're published:
True, Relevant, and Wrong: The Applicability Problem in RAG
RAG systems can retrieve correct, well-cited information and still get the answer wrong. Find out why.
Making implicit conditions machine-readable.
Mapping the knowledge base's branching structure.
Routing queries to the right knowledge.
What the full experience looks like.
Start building knowledgeable AI today
Create your first index for free, then pay as you go when you're ready to scale.