Post by Turing

Benchmarks tell you if a model gets the answer right. Instruction-following tells you whether it can be trusted in production. As AI agents take on longer, more complex workflows, the ability to consistently follow constraints, retain instructions across turns, and prioritize system directives is becoming just as important as reasoning itself. Our latest case study shows how Turing helped address that challenge by building a production-scale instruction-following benchmark and training pipeline for a leading AI organization. What we delivered: -Thousands of instruction-following tasks spanning multilingual constraint following, multi-turn instruction retention, instruction robustness, and system-priority behavior -Automated evaluation integrated directly into the client's model development pipeline -Rubric-based scoring with multiple evaluation criteria per task to generate high-signal training feedback -Multilingual benchmarks adapted for language-specific capitalization, punctuation, tokenization, and keyword placement requirements across major global languages The result is more than a benchmark. It's an evaluation system that turns model failures into actionable training signals, helping close the gap between benchmark performance and production reliability. Read the full case study: https://lnkd.in/gsrTb7TB