Search Results: AI_Engineering

Public AI benchmarks often mislead product decisions by compressing multidimensional performance into deceptive single-number scores. Engineering leaders reveal how to escape the benchmark trap by creating minimum viable internal evaluations aligned with actual business needs.

IBM's latest deep dive reveals how Retrieval-Augmented Generation (RAG) tackles LLMs' hallucination problem by grounding responses in real-world data. This hybrid architecture promises more accurate, verifiable AI outputs while sidestepping costly retraining—potentially transforming enterprise AI adoption.