Rag Performance Prediction for Question Answering

This project is a reference implementation for an academic paper (arXiv:24xx or similar, given the 7-day age). While the research addresses a critical pain point—deciding when RAG is worth the latency and token cost—the code itself lacks any commercial moat. With 0 stars and 3 forks, it currently serves as a reproducibility package rather than a software product. The 'novel supervised predictor' is a valuable algorithmic contribution, but it is highly likely to be absorbed into broader RAG evaluation frameworks like Ragas, Arize Phoenix, or TruLens within months. Furthermore, frontier lab providers (OpenAI, Anthropic) have a direct incentive to build this 'routing' logic into their APIs to optimize their own internal compute. The defensibility is low because the logic is easily reimplemented once the paper's findings are public, and it lacks the network effects or data gravity of an infrastructure-grade tool.