Last year, we watched a large enterprise rollout of a retrieval-augmented system falter in real time.
In the lab, it dazzled—precision, fluency, speed. But the moment it met the messy reality of production data, things unraveled.
A user asked for "clinical trials on type 2 diabetes in India" and got back… patient education pamphlets.
Confidence evaporated overnight. Adoption slowed to a crawl. The lesson was painful but clear: even the smartest language model is only as good as what it retrieves.
According to the Leung et al. study on RAG system failures, the core weaknesses rarely emerge in the generation stage—they begin in retrieval and chunking.
These early cracks—E1 Overchunking, E2 Underchunking, E3 Context Mismatch—lead directly to retrieval failures such as E4 Missed Retrieval, E5 Low Relevance, and E6 Semantic Drift.
Once these faults appear, they ripple forward through the pipeline, manifesting as hallucinations, incoherence, and user mistrust.
In short: if you don't retrieve well, everything else collapses.
The VQL Solution
This is precisely the foundation on which VQL (Vector Query Language) was built.
We designed it to eliminate those silent, cascading retrieval errors that undermine even the most powerful AI systems.
With VQL, you can upload any unstructured data—no rigid schema, no brittle chunking heuristics—and query it naturally:
FIND {(clinical trials) or (observational studies)} on {type 2 diabetes} in {India}Every query produces a three-tiered ranking:
- Tier 1results match both the logical and semantic intent (the gold standard).
- Tier 2relaxes to logical-only matches when semantics are ambiguous.
- Tier 3falls back to semantic-only retrieval for open-ended discovery.
Addressing RAG Errors
This hybrid model directly addresses Leung's taxonomy of RAG errors:
- E1–E3are neutralized by intelligent, meaning-aware chunking that preserves contextual integrity.
- E4–E6are mitigated by a retrieval pipeline that balances structure and semantics—avoiding both missed and irrelevant chunks.
The result is retrieval you can trust. Deterministic, interpretable, and consistent across runs.
Because in enterprise systems—healthcare, legal, finance, defense—approximation isn't just inconvenient, it's dangerous.
Key Insights
- • VQL replaces uncertainty with verifiable logic.
- • It doesn't merely find "something close"; it finds what's right.
- • The model doesn't hallucinate context because the retrieval stage grounds it in truth.
- • Speed without certainty is a liability.
Determinism, not approximation, is the future of enterprise AI.