Hallucinations can be amusing and even quite entertaining, butthey can also have severe repercussions if the results of the hallucination are employed in the drafting of a visa application, a research audit, or a legal brief. Developing a review engine that has the capability to avoid hallucinations at all times was the objective of our team. The solution that was built was a mix of six separate layers that comprised retrieval, language modeling, verification, and human supervision, among others. The analysis and the numbers are attached to this article. Why Plain RAG Falls Short Retrieval-Augmented Generation (RAG) is a technique that enables a model to "look things up"; nonetheless, there are still three failure scenarios that have yet to be resolved: missing documents, unverified reasoning, and a lack of calibrated confidence.  For evaluations that carry significant consequences, a more strict approach is required. Six Layers of Guard Rails Document Ingestion — Handling documents with several formats, redaction of personally identifiable information (PII), and token chunking at 400 to 800 tokensHybrid Retrieval — sparse + dense scoring blended as Document Ingestion — Handling documents with several formats, redaction of personally identifiable information (PII), and token chunking at 400 to 800 tokens Document Ingestion Hybrid Retrieval — sparse + dense scoring blended as Hybrid Retrieval S(q, d) = α × s_BM25(q, d)  +  (1 - α) × s_vec(q, d) S(q, d) = α × s_BM25(q, d)  +  (1 - α) × s_vec(q, d) S(q, d) = α × s_BM25(q, d)  +  (1 - α) × s_vec(q, d) where: where: where: - The BM25 score is represented by s_BM25(q, d) - The BM25 score is represented by - The BM25 score is represented by s_BM25(q, d) s_BM25(q, d) - s_vec(q, d) is the score indicating the similarity between the two vectors - s_vec(q, d) is the score indicating the similarity between the two vectors - s_vec(q, d) is the score indicating the similarity between the two vectors followed by maximal marginal relevance for the purpose of ensuring variety. 3. Grounded Generation — Generation that is grounded in reality: a prompt that is led by a rubric; a JSON schema; [C1] citations; [MISSING] deficiencies Grounded Generation [C1] [MISSING] 4. Verification — Every assertion and citation pair is examined by the natural language inference (NLI) model. Verification The following equation represents the G claim: G_claim = max over j [ p_entail(claim, C_j) ] G_claim = max over j [ p_entail(claim, C_j) ] G_claim = max over j [ p_entail(claim, C_j) ] 5. Confidence Calibration — Calibration of the Confidence Level—Temperature Scaling Drives The expected calibration error was reduced to 0.042. . Confidence Calibration 6. Human Oversight — two experts, blind arbitration, immutable audit log Human Oversight Figure 1 – System architecture from the paper. Figure 1 – System architecture from the paper. What the Benchmarks Say • Groundedness up from 0.71 → 0.91 • Hallucinations down 41 % • ~2 hours saved per dossier (43 % faster) • Expert acceptance ≈ 92 % Beyond Visas Switch out the rubric and the same stack that may be used to conduct literature surveys, compliance audits, or clinical case reviews.  The shared trait: judgments that are necessary to survive examination and scrutiny. Roadmap in Three Bullets Cross-lingual retrieval which involves triggering the first step which is to get the necessary information.Auto-generation of evidence templates for missing proofs using a combination of all the relevant data.Active-learning loop driven by expert edits to ensure that  the data is accurate. Cross-lingual retrieval which involves triggering the first step which is to get the necessary information. Auto-generation of evidence templates for missing proofs using a combination of all the relevant data. Active-learning loop driven by expert edits to ensure that  the data is accurate. Figure 2 – End-to-end workflow. Figure 2 – End-to-end workflow. Conclusion Verifiable pipelines are more important than larger models. A review system that is genuinely admissible in court—or at a visa office—is created through the combination of hybrid retrieval, hard-coded structure, automated verification, and human judgment, which together transform language technology.

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Evidence-Grounded Reviews: Building a Hybrid RAG + LLM Stack That Actually Proves Its Claims

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