The Need for Structure
Large Language Models excel at text generation but fail when specific output structures are required. This inherent unreliability blocks their use in dependable systems.
The Proxy Structuring Engine (PSE) solves this by enforcing structure *during* generation. Its high-performance state machine engine guides the LLM, ensuring the output perfectly matches your requirements.
How It Works
Define Structure - Specify the required output format using Pydantic, JSON Schema, function signatures, or custom state machine composition.
Compile Grammar - PSE translates the structure into an efficient state machine grammar.
Guide Generation - The engine integrates via generation hooks, masking invalid token choices based on the grammar's current state.
Ensure Correctness - The LLM only samples valid tokens. Token Healing adds robustness. The final output is always structurally compliant.
Applications
Reliable AI Agents - Enable agents with dependable tool use and predictable state flow (powers the Proxy Base Agent).
Consistent Data Formatting - Ensure valid JSON, XML, or other structures for APIs and data pipelines.
Accurate Function Calls - Generate perfectly formed arguments for function or API interactions.
Valid Code Generation - Produce syntactically correct code adhering to specified structures.
Benefits
Build Reliably - Create dependable AI systems suitable for production environments.
Increase Efficiency - Eliminate brittle post-processing, validation loops, and retries.
Unlock Potential - Enable complex workflows previously hindered by LLM unpredictability.
