GRID Enables Enterprise-Grade SQL Generation with LLMs

Large language models can generate SQL, but enterprise applications demand more than just plausible text. Outputs must be syntactically correct, adhere to role-based and schema-specific policies, offer provable guarantees, maintain performance with increasing generation length, and provide a compliance-grade audit trail. The new Grammar-Railed Decoding (GRID) engine addresses these challenges. GRID constrains LLM decoding by using an LALR(1) parser to generate exact next-token masks based on parser configurations. This approach ensures that forbidden verbs and identifiers are unreachable at the mask level by compiling role-based access control directly into the language grammar. The system provides guarantees for soundness, completeness, termination, and near-constant per-token cost. Implemented with Rust kernels, GRID achieves very fast per-token masking. Benchmarks on the Spider dataset show significant improvements in execution accuracy for constrained decoding, especially for smaller models, and a checker-guided repair pass further boosts performance for larger models. A hash-chained audit trail ensures tamper detection, making it suitable for regulated environments.