From Prompting to Programming: Making LLM Outputs More Predictable with Structure

Based on the open-source Symbolic Prompting framework. All benchmarks, datasets, and workflows are publicly available for verification. The Problem Most interactions with LLMs today look like this: I have a user who is 17 years old. Can they vote? Please analyze their age and tell me if they meet the requirement. And the output is often something like: “It depends on the country…” This isn’t wrong — but it’s not predictable. The model is interpreting intent, filling gaps, and defaulting to conversational behavior. A Different Approach: Treat Prompts as Logic Instead of asking, we can structure the prompt more like a program: [ROLE] ::= Age_Validator $age := 17 IF $age >= 18 THEN _result := "APROVED" ELSE _result := "REFUSED" ENDIF [CONSTRAINTS] { NO_ADD_COMMENTS_OR_PROSE, ONLY_PRINT_VALUE } [OUTPUT] ::= _result Observed result (multiple runs): REFUSED Same input → same output pattern. Quick Repro (Copy/Paste Test) You can test the difference yourself: 1. Natural language prompt • Ru