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Function Calling Harness, from 6.75% to 100% (Qwen Meetup Presentation)

Function Calling Harness, from 6.75% to 100% (Qwen Meetup Presentation)

by jhnam88·Apr 11, 2026·1 point·0 comments
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AI Analysis

●●SolidBig BrainNiche Gem

TypeScript compiler for LLM validation beats Instructor and Guidance on nested schemas.

Strengths
  • •TypeScript compiler infrastructure for deterministic LLM output validation is genuinely clever
  • •Concrete metrics (6.75% → 99.8%+) with specific parse/validate/feedback methodology
  • •Self-healing loops with schema-based type coercion handle recursive nested structures
Weaknesses
  • •This is a presentation about a technique, not a standalone product you can install today
  • •Structured output validation is crowded (Instructor, Guidance, LMQL already exist)
Category
AI/ML
Target Audience

Developers building AI agents with structured output requirements

Similar To

Instructor · Guidance · LMQL

Post Description

Presentation slides + writeup from Qwen Meetup Korea. How I turned 6.75% first-try tool calling success rate into 100% using a parse/validate/feedback loop — powered by a TypeScript compiler

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