2 results found
An in-depth review of the AI Evaluation Harness methodology reveals its critical importance for enterprise LLMs. Unlike qualitative reviews, it objectively measures correctness, unmasking models' dangerous tendency to be most confident when wrong.
Context Hub (`chub`) addresses LLM limitations by providing coding agents with curated, versioned documentation and skills via a CLI, augmented by local annotations and maintainer feedback. This article explores `chub`'s workflow and content model, then demonstrates building a companion relevance engine. This engine uses an additive reranking layer with extracted signals to significantly improve search accuracy for shorthand queries without altering `chub`'s core design.
