Sampling and Structured Outputs in LLMs
Large language models generate text token by token, but production applications need structured data. Modern constraint systems solve this by masking invalid tokens during generation, ensuring outputs conform to specific formats like JSON or custom grammars.
The Token Masking Approach
Structured output libraries work by intercepting the sampling process. When an LLM generates probabilities for its next token, these systems zero out probabilities for tokens that would violate the target format. This forces the model to only select valid continuations.
The process operates in real-time during inference:
- Model computes token probabilities
- Grammar parser determines valid next tokens
- Invalid tokens get masked (probability set to zero)
- Model samples from remaining valid tokens
Libraries like Guidance and LLGuidance implement this using Earley parsers, which handle context-free grammars efficiently enough to avoid slowing down inference.
Performance Considerations
Token masking introduces minimal overhead when implemented correctly. The mask computation runs on CPU while the GPU processes the forward pass. As long as parsing completes faster than the LLM inference step, constrained generation matches unconstrained speed.
However, this approach changes the probability distribution compared to unconstrained generation. When invalid tokens get redistributed to valid ones, the relative likelihood between valid options shifts. This can affect output quality, particularly for complex reasoning tasks.
Implementation Challenges
Different tokenizers split text differently, creating alignment issues between grammar rules and token boundaries. A JSON string might span multiple tokens, requiring careful handling of partial matches.
Vocabulary size also matters. Larger vocabularies mean more tokens to evaluate against grammar rules, though optimizations like precomputed masks help manage this complexity.
Alternative Approaches
Some teams use two-pass generation: let the model generate freely, then parse and fix the output with a second model. This preserves reasoning quality but doubles API costs.
Schema-aligned parsing offers another path - building error tolerance into parsers rather than constraining generation. This allows models to express uncertainty or partial information while still producing usable structured output.
Production Recommendations
For JSON output, most providers now offer built-in structured generation. OpenAI’s structured outputs and similar features handle common use cases reliably.
For custom formats, evaluate whether the constraint complexity justifies the implementation overhead. Simple grammars work well, but complex domain-specific languages may benefit from post-processing approaches.
Consider your quality requirements. If perfect syntax matters more than optimal content, constrained generation excels. If reasoning quality is paramount, two-pass generation may serve better.
Next Steps
Test structured output approaches against your specific use cases. Measure both syntactic correctness and semantic quality. The optimal solution depends on your performance requirements, cost constraints, and quality standards.