{"slug": "nexa-gauge-llm-evaluation-framework-with-per-node-scoring-controls", "title": "Nexa-gauge – LLM evaluation framework with per-node scoring controls", "summary": "Nexa-gauge, a graph-based evaluation framework for LLM and LVLM applications, has been released to replace ad-hoc manual checks with a repeatable pipeline that supports per-node scoring controls and deterministic caching. The system normalizes raw records into typed evaluation states, executes only required nodes for selected targets, and produces consistent per-case reports for prompt iteration, benchmark runs, and release gating. The framework combines LLM-as-a-judge semantic evaluation with targeted metrics including relevance, grounding, redteam, geval, and reference scoring.", "body_md": "# Introduction\n\n## Overview\n\nnexa-gauge is a graph-based evaluation system for LLM and LVLM application outputs. It replaces ad-hoc manual checks with a repeatable pipeline that can be run on local datasets or hosted datasets.\n\nAt a high level, nexa-gauge:\n\n- Normalizes raw records into a typed evaluation state.\n- Executes only the nodes required for the selected target.\n- Reuses prior node outputs through deterministic caching.\n- Produces a consistent per-case report for downstream tooling.\n\nThis architecture supports day-to-day prompt iteration, benchmark runs, and release gating with measurable quality and safety signals.\n\n## Why LLM-As-A-Judge Is Necessary\n\nExact-match metrics are useful but limited for modern generative systems. In many real tasks, multiple answers can be valid, quality depends on context use, and failure modes are semantic rather than lexical.\n\nLLM-as-a-judge provides scalable semantic evaluation by scoring outputs against explicit criteria. In nexa-gauge, this capability is combined with targeted metrics so teams can evaluate quality from multiple angles:\n\n`relevance`\n\nfor input-output alignment.`grounding`\n\nfor support in provided context.`redteam`\n\nfor safety and risk behavior.`geval`\n\nfor rubric-based judgment.`reference`\n\nfor overlap with known reference answers.\n\n## Execution Model And Caching\n\nnexa-gauge provides two operational modes:\n\n`run`\n\nexecutes the selected branch and returns final artifacts.`estimate`\n\ncomputes uncached eligible cost before execution.\n\nBoth modes follow the same branch-planning logic, which makes cost estimates actionable before you run full evaluations.\n\nCaching is route-aware and deterministic. Reuse occurs only when input content and routing semantics are unchanged. Changes to inputs, prompts, or model routing intentionally invalidate affected steps.\n\nPractical outcome:\n\n- Teams can estimate budget before execution.\n- Iterative runs avoid recomputing stable nodes.\n- Results remain reproducible under fixed inputs and model routes.\n\n## Architecture\n\n## Node Summary\n\n### Input And Orchestration\n\n| Node | Purpose |\n|---|---|\n`scan` | Normalizes record fields and initializes case state. |\n`eval` | Aggregates metric branches into a unified result. |\n`report` | Projects final output into a stable report contract. |\n\n### Utility Nodes\n\n| Node | Purpose |\n|---|---|\n`chunk` | Splits generated text for downstream extraction. `Semchunk` .. |\n`refine` | Removes, deduplicates, reranks, selects topk chunks. `mmr` |\n\n### Metric Essentials\n\n| Node | Purpose |\n|---|---|\n`claims` | Extracts atomic claims from generated output. |\n`geval_steps` | Resolves evaluation steps for GEval scoring. |\n\n### Metric Nodes\n\n| Node | Purpose |\n|---|---|\n`relevance` | Measures how directly claims answer the input. |\n`grounding` | Measures whether claims are supported by context. |\n`redteam` | Evaluates safety and policy risk using rubrics. |\n`geval` | Runs final rubric-driven LLM judging. |\n`reference` | Computes reference-based lexical metrics. |\n\n## Typical Workflow\n\n```\n# Estimate full evaluation cost for a dataset slice\nnexagauge estimate eval --input sample.json --limit 100\n\n# Run full evaluation and write per-case report files\nnexagauge run eval --input sample.json --limit 100 --output-dir ./report\n```\n\nFor dataset fields, accepted aliases, and metric activation rules, see the [Data Schema](/nexa-gauge/docs/data/schema).\n\nFor iterative development, repeated runs on unchanged inputs and routing should show high cache reuse and lower incremental latency.", "url": "https://wpnews.pro/news/nexa-gauge-llm-evaluation-framework-with-per-node-scoring-controls", "canonical_source": "https://harnexa.dev/nexa-gauge/docs/introduction", "published_at": "2026-05-30 19:50:37+00:00", "updated_at": "2026-05-30 20:16:57.048826+00:00", "lang": "en", "topics": ["large-language-models", "generative-ai", "ai-tools", "ai-safety", "mlops"], "entities": ["nexa-gauge", "LLM", "LVLM"], "alternates": {"html": "https://wpnews.pro/news/nexa-gauge-llm-evaluation-framework-with-per-node-scoring-controls", "markdown": "https://wpnews.pro/news/nexa-gauge-llm-evaluation-framework-with-per-node-scoring-controls.md", "text": "https://wpnews.pro/news/nexa-gauge-llm-evaluation-framework-with-per-node-scoring-controls.txt", "jsonld": "https://wpnews.pro/news/nexa-gauge-llm-evaluation-framework-with-per-node-scoring-controls.jsonld"}}