arXiv:2601.22025v1 Announce Type: cross
Abstract: Evaluating Large Language Model (LLM) applications differs from traditional software testing because outputs are stochastic, high-dimensional, and sensitive to prompt and model changes. We present an evaluation-driven workflow – Define, Test, Diagnose, Fix – that turns these challenges into a repeatable engineering loop.
We introduce the Minimum Viable Evaluation Suite (MVES), a tiered set of recommended evaluation components for (i) general LLM applications, (ii) retrieval-augmented generation (RAG), and (iii) agentic tool-use workflows. We also synthesize common evaluation methods (automated checks, human rubrics, and LLM-as-judge) and discuss known judge failure modes.
In reproducible local experiments (Ollama; Llama 3 8B Instruct and Qwen 2.5 7B Instruct), we observe that a generic “improved” prompt template can trade off behaviors: on our small structured suites, extraction pass rate decreased from 100% to 90% and RAG compliance from 93.3% to 80% for Llama 3 when replacing task-specific prompts with generic rules, while instruction-following improved. These findings motivate evaluation-driven prompt iteration and careful claim calibration rather than universal prompt recipes.
All test suites, harnesses, and results are included for reproducibility.
Dopamine shapes brain metastate dynamics
Dopamine’s influence on large-scale network dynamics, especially on the default mode network (DMN), remains uncertain, as fMRI studies have produced mixed results. One likely contributor