{"product_id":"llm-evaluation-specialist","title":"LLM Evaluation Specialist","description":"\u003cdiv\u003eA measurement engineer who transforms vague assertions about AI quality into quantified, reproducible evaluation systems that teams actually trust—with the psychometric rigor to distinguish genuine capability from pattern matching and the red-team instinct to find failure modes before users do.\u003c\/div\u003e\u003cdiv\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cstrong\u003eWhat you get:\u003c\/strong\u003e\u003c\/div\u003e\u003cdiv\u003e- The MEASURE LLM Evaluation methodology — 6-pillar framework from failure mapping to continuous evolution\u003c\/div\u003e\u003cdiv\u003e- Failure mode taxonomy design with business-impact prioritization and edge case stratification\u003c\/div\u003e\u003cdiv\u003e- Evaluation dataset engineering with contamination tracking, adversarial subset construction, and version control\u003c\/div\u003e\u003cdiv\u003e- Multi-layer scoring pipelines: deterministic checks, classifiers, LLM-as-judge with calibration protocols\u003c\/div\u003e\u003cdiv\u003e- Disaggregated performance analysis by task, difficulty, demographic, and content sensitivity — catching regressions aggregate metrics hide\u003c\/div\u003e\u003cdiv\u003e- Human annotation workflow design with inter-annotator agreement tracking and calibration sessions targeting kappa \u0026gt; 0.7\u003c\/div\u003e\u003cdiv\u003e- CI\/CD integration with quality gates, cost-aware evaluation tiers, and automated alerting on threshold breaches\u003c\/div\u003e\u003cdiv\u003e- RAG and multi-turn agent evaluation covering retrieval faithfulness, turn-level coherence, and tool-use correctness\u003c\/div\u003e\u003cdiv\u003e- Safety and fairness red-teaming protocols with systematic attack taxonomies and regulatory alignment mapping\u003c\/div\u003e\u003cdiv\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cstrong\u003eHow it works:\u003c\/strong\u003e\u003c\/div\u003e\u003cdiv\u003eDrop into Claude, ChatGPT, Cursor, or any AI tool. Bring your real evaluation problem — a model migration with unclear quality impact, a RAG system you can't measure, a safety gap nobody knows how to test, a fairness audit requirement. It thinks like an engineer who's built evaluation pipelines across retrieval systems, multi-turn agents, and customer-facing copilots under shipping pressure.\u003c\/div\u003e\u003cdiv\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cstrong\u003eBest used with:\u003c\/strong\u003e\u003c\/div\u003e\u003cdiv\u003eBundles or prompts related to AI quality assurance, LLM product development, and evaluation infrastructure.\u003c\/div\u003e","brand":"penguin tree ai","offers":[{"title":"Default Title","offer_id":51992837947694,"sku":"llm-evaluation-specialist","price":5.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0982\/4203\/6014\/files\/llm-evaluation-specialist_176f0ff5-e737-440a-9e8a-58491d5de9a5.png?v=1779766908","url":"https:\/\/penguintree.ai\/products\/llm-evaluation-specialist","provider":"penguin tree ai","version":"1.0","type":"link"}