klotz: machine learning architecture*

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  1. * **Structured Outputs:** Uses grammar-constrained decoding (logit biasing/masking) to enforce strict JSON schema compliance during inference. Best for deterministic data transformation.
    * **Function Calling:** Utilizes instruction tuning to enable model reasoning over tool definitions. Best for agentic workflows and external state mutation.

    | Feature | Structured Outputs | Function Calling |
    | :--- | :--- | :--- |
    | **Mechanism** | Constrained decoding (Grammar/Regex) | Instruction-tuned intent detection |
    | **Reliability** | 100% Schema Compliance | Probabilistic (requires retry logic) |
    | **Primary Use Case** | ETL, Query Gen, Reasoning traces | API Triggers, RAG, Task Routing |
    | **Latency/Cost** | Low overhead; optimized decoding | Higher overhead due to tool-definition tokens |

    * **ETL & Extraction:** Use Structured Outputs to ensure downstream parsers never fail on malformed JSON.
    * **Agentic Loops:** Use Function Calling for multi-turn interactions where the model must decide *which* tool to invoke based on context.
    * **Hybrid Pattern (Controller/Formatter):** Deploy a "Function Calling" agent as the **Controller** to select tools, then pipe results through a "Structured Output" layer as the **Formatter** to ensure clean data ingestion into databases or UIs.

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