Verification layers sort outputs by trust rather than fixing errors. In a benchmark of six reliability layers, voting and self-correction failed to recover accuracy - they only withheld doubtful records. Production systems gain more reliability by replacing generation with deterministic lookups, tracking yield over raw accuracy, and resolving extraction bottlenecks before stacking checks. By Pushpdeep Mishra, Wejdan Bagais
Read the original at InfoQ: AI, ML and data engineering: Article: LLM Reliability Layers Under Test: Accuracy, Yield, and the Limits of Verification
How OpenSmartRoute helps
A published score is a starting point, not the answer for one team's prompts. 'osr eval' measures routing accuracy on the team's own data and can fail a build when it drops. The hosted platform adds public rankings built from real traffic, so a number can be checked against what the model does in use.
For a team that routes through the router, a release like this is one catalogue entry. The new model competes on the next request against what the team already runs, on quality, cost and speed. If it answers well it earns more traffic; if it fails it gets less, with no code change in the app.
