Most RAG systems have two problems that nobody talks about enough.

The first is semantic drift — your retriever pulls in chunks that look relevant (high cosine score) but don't actually answer the question causally. Ask "Why did Lehman Brothers collapse?" and you'll get back chunks about the 2008 housing crisis — same vocabulary, but those are the consequences, not the cause. Cosine similarity can't tell the difference.

The second is context poisoning — even if each individual chunk is okay, a window full of semi-relevant chunks confuses the LLM. It attends to all of them, averages them out, and hallucinates.

VORTEXRAG fixes both. It's a 7-layer pipeline I built specifically around these two failure modes. Each layer has a specific job: