[P] Prompt optimization for analog circuit placement — 97% of expert quality, zero training data
TL;DR Highlight
Prompt optimization achieves 97% of expert quality on analog circuit placement with zero training data — learns from failure-to-success pairs iteratively
Who Should Read
Engineers applying LLMs to specialized tasks; AI developers interested in automatic prompt optimization
Core Mechanics
- VizPy prompt optimizer: iteratively learns from failure→success pairs to improve LLM layout reasoning
- Applied to analog IC placement (spatial reasoning + multi-objective: matching, parasitics, routing) — hard benchmark with no AI tools
- Zero domain-specific training data needed to achieve 97% of expert quality
Evidence
- VizPy blog (vizops.ai/blog/prompt-optimization-analog-circuit-placement) — methodology and results publicly available
How to Apply
- For tasks with limited domain-specific data, apply prompt optimizer + failure-to-success feedback loop pattern
- For spatial reasoning and multi-objective optimization problems, iterative prompt improvement may be more effective than few-shot examples
Terminology
아날로그 IC 배치(Analog Circuit Placement)Semiconductor chip design stage — high-difficulty task requiring simultaneous optimization of component placement, circuit matching, and parasitic characteristics