Why RAG Pipelines Still Let LLM Hallucinations Slip Into Production

A technical piece argues that LLM hallucinations are an inherent consequence of autoregressive models, which predict probable tokens rather than verify truth. It notes that fabricated outputs often look identical to accurate ones, so standard error handling misses them. The article contends that retrieval-augmented generation can still fail when retrieved sources are outdated or misapplied, and that evaluators checking groundedness may approve a faithful summary of the wrong document. It recommends tighter generation settings, metadata filtering, fallback responses for missing context, and treating model output as untrusted input validated by schema checks, AST parsing, and human review.