Show HN: 100% LLM Accuracy–No Fine-Tuning, JSON Only

One of the persistent challenges in local LLM deployment is controlling model output and eliminating hallucinations, typically requiring expensive fine-tuning pipelines. This project demonstrates that enforcing structured JSON schemas can achieve near-perfect accuracy without touching model weights, making it particularly valuable for resource-constrained environments.

For local deployment practitioners, this approach eliminates an entire category of computational overhead. Instead of fine-tuning models (which requires significant VRAM, storage, and training time), you constrain inference output through schema validation and guided generation. This means smaller models can achieve the reliability of fine-tuned variants, fitting comfortably within mobile, edge, and consumer hardware budgets.

The benchmark results suggest this technique works across model sizes and architectures, making it a practical go-to pattern for any local LLM application requiring deterministic, structured outputs—from API integrations to data extraction pipelines.

Source: Hacker News · Relevance: 8/10