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llm-evaluation

Case study of a pre-alpha Algebra I tutoring platform focused on hybrid AI/deterministic architecture, multi-provider LLM evaluation, safety controls, and measured cost/latency tradeoffs.

Production-grade toolkit for evaluating RAG (Retrieval-Augmented Generation) pipelines.

A small, transparent experiment testing whether language models distinguish solvable prompts from prompts containing missing or contradictory information, and whether their stated confidence tracks correctness

Model Verification Layer is a modular system for evaluating, comparing, and validating the behavior of large language models using structured benchmarks, logic consistency checks, cross-model consensus analysis, and policy-aware constraints. It provides a transparent framework for understanding how different AI models perform under identical conditions, enabling more reliable model selection, safer deployment, and user-adaptive decision making. https://roxanneardary.com/model-verification-layer/

Most LLM reasoning benchmarks come from Western math, English logic, or code. Bazi-Bench tests multi-step rule-following inference in a different formal system: traditional Chinese Ba Zi. Frozen tables, Python reference impl, gold CoT cases, all mechanically verifiable.