New Define-Test-Diagnose-Fix workflow nails 90% accuracy evaluating LLMs - no more guessing if your prompt tweaks actually helped.
Prompt engineering feels like black magic because your evals are broken. This new framework fixes that with 90% reliable LLM assessments.
Researchers unveiled the Minimum Viable Evaluation Suite (MVES) and a Define-Test-Diagnose-Fix workflow that achieves 90% accuracy in evaluating general LLMs, RAG systems, and agentic flows. Tested on Llama 3 8B and Qwen 2.5 7B via Ollama, it exposes how ‘improved’ prompts can tank extraction (100% to 90%) or RAG compliance (93% to 80%) despite better instruction-following.[3]
Developers waste weeks chasing benchmark gains that vanish in production. MVES provides tiny, structured test suites for continuous iteration, blending automated checks, human rubrics, and LLM judges while spotting their failure modes. It’s a repeatable engineering loop for the high-variance world of LLMs.[3]
This tops ad-hoc evals like HELM or BigBench, offering tiered assessment for RAG/agents where traditional metrics fail. Open materials mean instant reproducibility, outpacing proprietary dashboards from Scale or Honeycomb.[3]
Download the public test suites and results today. Run MVES on your next prompt tweak - quantify trade-offs before deployment. Ready to make eval-driven development your default? This could be the missing piece.
Source: Quantum Zeitgeist