A Dual-Layer Framework for LLM Hallucination Mitigation: Design and Preliminary Evaluation

Large language models (LLMs) produce fluent but factually incorrect statements, known as hallucinations, which are risky in medicine, law and finance. Retrieval-augmented generation (RAG) conditions the input but does not check the output, whereas post-generation verifiers check the output but do not shape generation. We describe a two-layer framework that requires no fine-tuning of the base LLM. A Pre-Generation Control Layer (PGCL) classifies each query by domain and risk, retrieves evidence for high-risk queries, builds a constrained prompt and sets a risk-dependent decoding temperature. A Post-Generation Verification Layer (PGVL) splits the answer into atomic claims, scores each against a hierarchical Tree-RAG index with an LLM judge and a retrieval-similarity signal, and compares the resulting TruthScore with a threshold conditioned on the semantic entropy of sampled answers. Failing claims are handled by a Surgical Self-Correction Loop. We report a small pilot (50 HotpotQA and 25 TruthfulQA questions, Llama-3.3-70B) and, mainly, what it reveals. For 86% of verified claims no evidence was retrieved, so the judge largely assessed its own output. On identical claims, the entropy-conditioned threshold changed 1.6-9.9% of accept decisions, all toward rejection. Of 120 rejected claims, 85 ended as "cannot verify" and only 7 of 30 genuine rewrites passed re-verification. The gold HotpotQA answer appeared in 8 of 50 answers before verification and 9 of 50 after. We therefore find no measurable improvement in answer correctness, and we document confounds (a shared fact memory, unstable risk classification, run-to-run variance) that future evaluations must control.