Large language models hallucinate persistently, which makes them unsuitable for zero-tolerance domains — contexts where a factual error becomes an audit finding, a legal liability, or a mission-impacting defect. Retrieval-augmented generation reduces hallucination but cannot eliminate it, because output formation stays stochastic.
Deterministic-First inverts that flow. Complete responses — including citations, identifiers, and quantitative values — are composed programmatically from authoritative, version-controlled repositories before any optional model invocation. The model is reduced from a primary generator to a constrained refiner, permitted only to improve style under an explicit prohibition on introducing new facts, citations, identifiers, or quantitative values, and gated by a deterministic, fail-closed validation layer. Within its protected-field scope, the pattern eliminates LLM-sourced factual hallucination by construction — not by reducing its probability, but by removing the model’s ability to author a fact at all.
In zero-tolerance domains, plausible language without traceable provenance is equivalent to being wrong. The gap these environments demand is forensic auditability — decomposable proof that each factual claim came from an authoritative record. RAG leaves output formation stochastic (retrieval misses, synthesis errors, misattribution, fabrication). Fine-tuning doesn’t guarantee citation correctness. Constrained decoding shapes structure, not truth. Post-hoc verification yields a confidence score, not an audit trail.
A user asked a routine question: how often should critical systems be scanned? An intent misclassification routed it to the generative path, and the model produced fluent, authoritative-sounding guidance — “6–12 months” — for a requirement that did not exist in any authoritative source. The failure wasn’t missing context. It was an architecture that let a probabilistic generator author a fact-bearing answer at all.
Three constraints define the pattern: authoritative source binding (no fact selection inside a model call), programmatic assembly first (the complete template is built before any refinement), and constrained refinement with validation (the model may rewrite for clarity; a deterministic gate rejects any refinement that touches a protected field and falls back to the template).
An observational deployment supporting NIST SP 800-53 Rev 5, SP 800-171 Rev 3, and CNSSI 1253 control documentation across 10,000 compliance artifacts.
These are operational findings within a bounded protected-field scope — not a controlled comparison. A head-to-head evaluation against RAG, guardrailed-RAG, and verifier-loop baselines is specified as a protocol and reserved for future work. Adversarial tests exercised representative injection and validation-evasion cases; no accepted protected-field modifications were observed in the tested suite.
Five commitments bound the contribution, and should be read with every use of the word “elimination.”
The guarantee covers a defined protected-field scope — identifiers, citations, control tokens, quantitative values — not the general semantic truth of every sentence.
The pattern assumes version-controlled authoritative sources. If a source is wrong, the system faithfully reproduces the error. Source quality is a customer governance responsibility.
The LLM is treated as a component outside the trusted computing base — useful for refinement, never granted factual authority.
The guarantee concerns outputs that pass the deterministic validation gate — not arbitrary, unvalidated model output.
Selecting the wrong authoritative record remains a first-class risk — measured separately, and distinct from hallucination.
The pattern addresses factual accuracy. Data sovereignty is a function of deployment topology (local vs. cloud inference) and is orthogonal to the architectural guarantee.
Production telemetry over 18 months — validator-rejection, audit-integrity, and adversarial analyses — not a randomized baseline comparison.
A head-to-head protocol against RAG, guardrailed-RAG, and verifier-loop baselines is defined and reserved for a subsequent study.
Reference algorithms, schema definitions, and a synthetic evaluation-corpus design support independent replication; the production implementation remains proprietary.
The pattern generalizes wherever a factual error carries externally enforced consequences and outputs must be traceable to an authoritative record.
We share the paper — including the validation-gate specification, threat model, and the synthetic evaluation-corpus design — with federal and regulated-industry teams on request.