Working notes on the parts of AI that break under institutional load. Retrieval, citation faithfulness, multi-hop reasoning, knowledge conflicts. Cited from the literature, refined in production.
These systems can appear to succeed while failing at the level of evidence and reasoning. The gap between a system that works and one that merely appears to work is not an academic distinction.
AI · RAG · Opinion · Enterprise AI
RAG evaluation suffers from Diagnostic Collapse: single scalar scores cannot distinguish retrieval failure from grounding failure.
RAG · Evaluation · LLM · Grounding
Multi-hop RAG fails even when correct evidence is retrieved. 73-84% of errors are reasoning failures, not retrieval failures.
RAG · Multi-Hop · Reasoning · LLM
RAG systems exhibit Temporal-Semantic Mismatch from multi-layer structural degradation. RAG mitigates but does not resolve the dynamic knowledge problem.
RAG · Temporal Knowledge · LLM · Time-Sensitive
RAG assumes models prioritize retrieved evidence over parametric memory. Empirical studies show this fails under knowledge conflict in dynamic domains.
RAG · Knowledge Conflicts · Parametric Memory · LLM
Citation-based RAG aims to improve groundedness, yet citations do not guarantee claims are supported. This note explains why RAG still hallucinates.
RAG · Hallucination · Citation · LLM
Do long-context LLMs replace retrieval? Evidence shows complementary roles in knowledge-intensive AI.
RAG · Long Context · LLM · Retrieval