[{"data":1,"prerenderedAt":237},["ShallowReactive",2],{"blog-post-blog_en-llm-evaluations-fuer-produktteams":3},{"id":4,"title":5,"body":6,"cover":221,"date":222,"description":223,"draft":224,"extension":225,"meta":226,"navigation":227,"path":228,"seo":229,"stem":230,"tags":231,"__hash__":236},"blog_en\u002Fen\u002Fblog\u002Fllm-evaluations-fuer-produktteams.md","LLM Evaluations for Product Teams: Measuring AI Feature Quality",{"type":7,"value":8,"toc":216},"minimark",[9,13,18,21,24,59,62,66,69,72,167,170,173,193,197,200,203,212],[10,11,12],"p",{},"LLM evaluations become relevant once AI features move beyond experimentation. When a support agent writes replies, a copilot summarises contract data, or an internal tool prepares decisions, subjective testing in a chat window is no longer enough. Product and engineering need reproducible quality boundaries.",[14,15,17],"h2",{"id":16},"what-llm-evaluations-actually-measure","What LLM Evaluations Actually Measure",[10,19,20],{},"LLM evaluations are not a classic unit test suite with a simple true or false result. They assess whether an AI system remains useful, safe and economically viable in a specific workflow.",[10,22,23],{},"For growing product teams, five dimensions matter most:",[25,26,27,35,41,47,53],"ul",{},[28,29,30,34],"li",{},[31,32,33],"strong",{},"Domain correctness:"," Do the answer, sources and assumptions match the business domain?",[28,36,37,40],{},[31,38,39],{},"Policy compliance:"," Does the system violate privacy, security or brand rules?",[28,42,43,46],{},[31,44,45],{},"Robustness:"," Does the result remain stable when users phrase requests differently or hit edge cases?",[28,48,49,52],{},[31,50,51],{},"Cost and latency:"," Is a quality improvement bought through disproportionately expensive model calls?",[28,54,55,58],{},[31,56,57],{},"Handover:"," Does the system know when it should involve a human?",[10,60,61],{},"LLM-as-a-judge can help evaluate open-ended answers at scale. But it does not remove business accountability. A judge model needs clear rubrics, comparison data and regular human sampling, otherwise the team is only measuring another model opinion.",[14,63,65],{"id":64},"where-product-teams-should-start-with-evaluations","Where Product Teams Should Start With Evaluations",[10,67,68],{},"The most common mistake is measuring only after rollout. By then, baselines are missing, poor answers have reached customers and model changes become judgement calls.",[10,70,71],{},"A pragmatic starting point is a small, versioned evaluation set for one workflow:",[73,74,79],"pre",{"className":75,"code":76,"language":77,"meta":78,"style":78},"language-yaml shiki shiki-themes github-light github-dark","llm_eval_suite:\n  workflow: support_ticket_answer\n  dataset: \"80 anonymised real cases\"\n  checks: [\"groundedness\", \"policy_compliance\", \"handover_needed\"]\n  release_gate: \"no regression on critical cases\"\n  owner: product_engineering\n","yaml","",[80,81,82,95,108,119,145,156],"code",{"__ignoreMap":78},[83,84,87,91],"span",{"class":85,"line":86},"line",1,[83,88,90],{"class":89},"s9eBZ","llm_eval_suite",[83,92,94],{"class":93},"sVt8B",":\n",[83,96,98,101,104],{"class":85,"line":97},2,[83,99,100],{"class":89},"  workflow",[83,102,103],{"class":93},": ",[83,105,107],{"class":106},"sZZnC","support_ticket_answer\n",[83,109,111,114,116],{"class":85,"line":110},3,[83,112,113],{"class":89},"  dataset",[83,115,103],{"class":93},[83,117,118],{"class":106},"\"80 anonymised real cases\"\n",[83,120,122,125,128,131,134,137,139,142],{"class":85,"line":121},4,[83,123,124],{"class":89},"  checks",[83,126,127],{"class":93},": [",[83,129,130],{"class":106},"\"groundedness\"",[83,132,133],{"class":93},", ",[83,135,136],{"class":106},"\"policy_compliance\"",[83,138,133],{"class":93},[83,140,141],{"class":106},"\"handover_needed\"",[83,143,144],{"class":93},"]\n",[83,146,148,151,153],{"class":85,"line":147},5,[83,149,150],{"class":89},"  release_gate",[83,152,103],{"class":93},[83,154,155],{"class":106},"\"no regression on critical cases\"\n",[83,157,159,162,164],{"class":85,"line":158},6,[83,160,161],{"class":89},"  owner",[83,163,103],{"class":93},[83,165,166],{"class":106},"product_engineering\n",[10,168,169],{},"The set must not contain only success cases. It needs difficult customer tickets, ambiguous requests, missing context, forbidden data requests and examples where the correct answer is a clean refusal or handover.",[10,171,172],{},"Before automating evaluation, teams should make three decisions:",[25,174,175,181,187],{},[28,176,177,180],{},[31,178,179],{},"Which failures are business-critical?"," An imprecise phrase is different from a wrong price, a privacy breach or unauthorised access.",[28,182,183,186],{},[31,184,185],{},"Who maintains the examples?"," Evaluation data becomes stale when products, policies and customer segments change.",[28,188,189,192],{},[31,190,191],{},"How does an evaluation block a release?"," Without thresholds, evals become reports rather than quality gates.",[14,194,196],{"id":195},"why-this-matters","Why This Matters",[10,198,199],{},"LLM evaluations make AI quality negotiable and verifiable. Without them, product decisions depend on demos, individual opinions and manual samples. That is not enough when a feature creates cost, influences customer communication or carries compliance risk.",[10,201,202],{},"For decision-makers, the economic point is clear: poor AI answers create support effort, loss of trust, rework and sometimes legal exposure. Good evaluations reduce these follow-up costs because model changes, prompt changes, retrieval updates and new tool calls are tested against real cases before rollout.",[10,204,205,206,211],{},"Growing teams should therefore treat LLM evaluations as product quality, not research. Small datasets, clear rubrics, regular human reviews and release gates are often enough to start. An ",[207,208,210],"a",{"href":209},"\u002Fen\u002F#packages","Architecture & AI Review"," can assess whether evaluations, observability and backend boundaries fit together before AI features scale.",[213,214,215],"style",{},"html pre.shiki code .s9eBZ, html code.shiki .s9eBZ{--shiki-default:#22863A;--shiki-dark:#85E89D}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":78,"searchDepth":97,"depth":97,"links":217},[218,219,220],{"id":16,"depth":97,"text":17},{"id":64,"depth":97,"text":65},{"id":195,"depth":97,"text":196},null,"2026-06-17","LLM evaluations help product teams control quality, risk and cost of AI features before rollout and make model changes safer for users.",false,"md",{},true,"\u002Fen\u002Fblog\u002Fllm-evaluations-fuer-produktteams",{"title":5,"description":223},"en\u002Fblog\u002Fllm-evaluations-fuer-produktteams",[232,233,234,235],"AI","Software Quality","Engineering Leadership","Governance","v_7f7ZG4hG031lTPkoFewvgCyrBuncyfpbGqDzmKJwk",1783430349863]