In enterprise deployment, large language models can generate SQL, but outputs must ensure more than plausible text: they must be syntactically valid, respect role and schema policies, provide provable guarantees, avoid slowdowns as generations grow, and leave a compliance-grade record of every decision. We present GRID (Grammar-Railed Decoding), a grammar-constrained decoding engine that determines exact next-token masks based on parser configurations (lexer scan state x LALR(1) stack) rather than token sequences, leveraging the incrementally advanced LALR(1) parser as a viable-prefix oracle.
LLM tokens are connected to grammar terminals through a byte-level trie walk with a context-independent/context-dependent split, ensuring cache-key soundness by construction. Role-based access control is compiled into the language: role projections subset the grammar's productions and schema lexicons restrict identifier terminals, making forbidden verbs and identifiers unreachable at the mask level. We state four guarantees (soundness, completeness, termination, and near-constant per-token cost) with explicit preconditions and each paired with a test or benchmark.
Rust kernels bring the per-token mask to a median of 3.6-6.7 microseconds, ahead of llguidance at p50 and p90 on two tokenizers with zero false rejects; per-token guard cost remains position-flat at n=16,000. On the Spider dataset, constrained decoding is worth +13 execution-accuracy points at 0.5B, and a checker-guided repair pass over the provably mask-unenforceable residue (column-level policy) lifts a 7B model to 94.5% executable. A hash-chained per-token audit trail replays bit-identically with 100% tamper detection. We plainly state what the mask cannot do (distribution faithfulness, column-level RBAC, non-LALR(1) languages) and where measured cost remains.
Blogger's Review: GRID introduces an innovative solution for enterprise SQL generation through grammar-constrained decoding, ensuring that the generated SQL is not only valid but also adheres to specific role and policy requirements. The implementation of this technology helps mitigate the risk of generation errors, enhancing system security and compliance, showcasing the potential and challenges in the application of large language models.