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error route","\u002Fauth\u002Ferror",{},"data\u002Fshared\u002Furls","NLkVE1lIUws518K-1QsSB6T-GvlXYIzcRL1HDhqarGQ",{"id":664,"title":665,"author":666,"authorPhoto":667,"authorPhotoAlt":668,"authorSlug":669,"body":670,"buttonText":993,"buttonUrl":668,"category":994,"date":995,"description":996,"draft":997,"editorsPick":997,"extension":998,"featured":997,"image":999,"imageAlt":668,"meta":1000,"navigation":32,"ogImage":668,"ogImageAlt":668,"path":1001,"seo":1002,"stem":1003,"tags":668,"__hash__":1004},"blog\u002Fblog\u002Fai-evaluation.md","How to evaluate AI: a practical guide for building trustworthy systems","The Pieces Team","https:\u002F\u002Fstorage.googleapis.com\u002Fpieces-marketing-website\u002Fimages\u002Fblog\u002Fai-evaluation\u002Fauthor.png",null,"the-pieces-team",{"type":671,"value":672,"toc":962},"minimark",[673,677,680,683,686,691,694,697,700,703,714,719,722,731,734,738,741,749,753,756,764,768,771,775,778,786,789,798,806,809,813,817,820,824,832,836,839,843,847,850,859,863,866,870,879,883,887,890,894,897,906,910,913,917,920,924,927,931,934,938,941,950,953,956,959],[674,675,676],"p",{},"As AI systems become more powerful and widespread, one of the biggest challenges isn’t building better models – it’s knowing how to evaluate them well.",[674,678,679],{},"Traditional software testing is clear-cut: inputs go in, and you check if outputs match expectations.",[674,681,682],{},"But with AI, it’s more complex.",[674,684,685],{},"You’re dealing with probabilities, emergent behaviors, and use cases that often defy a simple pass\u002Ffail test.",[687,688,690],"h2",{"id":689},"the-evaluation-challenge","The evaluation challenge",[674,692,693],{},"Conventional software testing is all about determinism. Feed the app some inputs, and it should produce the same outputs every time. The entire behavior space is well-defined, so it’s easy to cover with tests.",[674,695,696],{},"AI doesn’t play by those rules.",[674,698,699],{},"The same prompt might give you different responses depending on the day. And what counts as a “correct” answer?",[674,701,702],{},"That’s often fuzzy.",[674,704,705,706,713],{},"Consider how OpenAI’s ChatGPT handles ",[707,708,712],"a",{"href":709,"rel":710},"https:\u002F\u002Fbytebridge.medium.com\u002Fdeep-research-capabilities-comparing-chatgpt-perplexity-grok-and-kompas-ai-012d643fef5a",[711],"nofollow","factual vs. conversational requests",".",[715,716,718],"h3",{"id":717},"accuracy","Accuracy",[674,720,721],{},"Yes, accuracy still matters but it’s more nuanced.",[674,723,724,725,730],{},"In ",[707,726,729],{"href":727,"rel":728},"https:\u002F\u002Fpieces.app\u002Fblog\u002Flarge-language-models",[711],"language models",", you’re checking logic, facts, and tone.",[674,732,733],{},"In recommendation systems, accuracy might mean engagement rates, not just click-throughs. And \"right\" can vary by audience or context.",[715,735,737],{"id":736},"reliability","Reliability",[674,739,740],{},"How does the model hold up across edge cases, different input types, or over time? You want models that don’t break the moment they meet ambiguity or variation.",[674,742,743,748],{},[707,744,747],{"href":745,"rel":746},"https:\u002F\u002Fwww.paloaltonetworks.com\u002Fcyberpedia\u002Fnist-ai-risk-management-framework",[711],"NIST’s AI Robustness Framework"," is a helpful guide here.",[715,750,752],{"id":751},"safety","Safety",[674,754,755],{},"Bias, toxicity, hallucination – these are real risks. Safety testing checks for unintended consequences, especially in sensitive domains like healthcare or finance.",[674,757,758,763],{},[707,759,762],{"href":760,"rel":761},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fconstitutional-ai",[711],"Constitutional AI"," shows one approach to building safety into models.",[715,765,767],{"id":766},"usability","Usability",[674,769,770],{},"Even accurate systems are useless if they’re confusing or burdensome to use. Usability is about how helpful, intuitive, and empowering the AI feels in real-world workflows.",[687,772,774],{"id":773},"evaluation-methodologies","Evaluation methodologies",[674,776,777],{},"Use benchmarks for quick, reproducible comparisons. But remember: they’re not the full picture.",[674,779,780,785],{},[707,781,784],{"href":782,"rel":783},"https:\u002F\u002Fhuggingface.co\u002Fopen-llm-leaderboard",[711],"Hugging Face’s Leaderboard"," is great for baseline comparisons, but real-world use often tells a different story.",[674,787,788],{},"Sometimes only a human can judge creativity, nuance, or tone. This adds richness to evaluations but also bias, cost, and inconsistency.",[674,790,791,792,797],{},"OpenAI’s ",[707,793,796],{"href":794,"rel":795},"https:\u002F\u002Fopenai.com\u002Findex\u002Finstruction-following\u002F",[711],"human preference tuning"," is a prime example of where this shines.",[674,799,800,801,713],{},"Try to break the model on purpose. Red teaming, prompt injection, and edge-case testing all help uncover vulnerabilities before real users do. Read about Anthropic’s ",[707,802,805],{"href":803,"rel":804},"https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fanthropics-responsible-scaling-policy",[711],"adversarial testing protocol",[674,807,808],{},"The other part is that the best tests happen post-launch. User feedback, engagement trends, and error logs all tell you what’s really going on. Make sure your system learns from that data.",[687,810,812],{"id":811},"the-measurement-problem","The measurement problem",[715,814,816],{"id":815},"quantitative-metrics","Quantitative metrics",[674,818,819],{},"Numbers are easy to track and compare, but they can be misleading if they oversimplify. Be careful not to over-optimize for a narrow score and lose sight of holistic quality.",[715,821,823],{"id":822},"qualitative-assessment","Qualitative assessment",[674,825,826,827,713],{},"Subjective reviews help you evaluate appropriateness, tone, or creativity. But they’re harder to standardize. Learn from ",[707,828,831],{"href":829,"rel":830},"https:\u002F\u002Fai.google\u002Fprinciples\u002F",[711],"Google’s UX principles for AI",[715,833,835],{"id":834},"composite-evaluation","Composite evaluation",[674,837,838],{},"You’ll rarely have a single metric that tells the whole story. Accuracy, latency, safety, usability, they often trade off. The right composite score depends on your use case and goals.",[687,840,842],{"id":841},"context-dependent-evaluation","Context-dependent evaluation",[715,844,846],{"id":845},"domain-specificity","Domain specificity",[674,848,849],{},"An AI that works great in one domain (like customer support) may flop in another (like legal or medical). Tailor your metrics and tests to the problem space.",[674,851,852,853,858],{},"The ",[707,854,857],{"href":855,"rel":856},"https:\u002F\u002Fwww.fda.gov\u002Fmedical-devices\u002Fsoftware-medical-device-samd\u002Fartificial-intelligence-and-machine-learning-software-medical-device",[711],"FDA’s guidelines for medical AI"," are a good model.",[715,860,862],{"id":861},"user-variability","User variability",[674,864,865],{},"What works for power users might confuse beginners. Test with a diverse range of users to avoid blind spots.",[715,867,869],{"id":868},"cultural-and-linguistic-context","Cultural and linguistic context",[674,871,872,873,878],{},"Bias and misalignment can emerge in different languages or cultural settings. Stanford’s research on ",[707,874,877],{"href":875,"rel":876},"https:\u002F\u002Fcrfm.stanford.edu\u002F2023\u002F03\u002F13\u002Falpaca.html",[711],"multilingual bias in LLMs"," highlights this.",[687,880,882],{"id":881},"the-evolution-of-evaluation-standards","The evolution of evaluation standards",[715,884,886],{"id":885},"dynamic-benchmarks","Dynamic benchmarks",[674,888,889],{},"Instead of static test sets, think rotating benchmarks and procedurally generated tests to prevent overfitting.",[715,891,893],{"id":892},"emergent-capability-assessment","Emergent capability assessment",[674,895,896],{},"Some models do surprising things you didn’t plan for. Be ready to spot, test, and evaluate capabilities that weren’t part of the original scope.",[674,898,899,900,905],{},"Check out ",[707,901,904],{"href":902,"rel":903},"https:\u002F\u002Fatlregional.github.io\u002Fproj-eval\u002F",[711],"ARC’s evaluations"," of general agent intelligence.",[715,907,909],{"id":908},"long-term-impact-evaluation","Long-term impact evaluation",[674,911,912],{},"Short-term performance doesn’t always predict long-term value. Consider how the system affects workflows, user trust, and decision-making over months or years.",[715,914,916],{"id":915},"continuous-monitoring","Continuous Monitoring",[674,918,919],{},"Evaluation isn’t a one-time gate. Keep tracking performance after deployment. Use automated dashboards, alerts, and human-in-the-loop reviews.",[715,921,923],{"id":922},"multi-stakeholder-assessment","Multi-stakeholder assessment",[674,925,926],{},"Bring in different viewpoints: technical leads, designers, domain experts, and end-users. You’ll get a fuller picture of performance.",[715,928,930],{"id":929},"risk-based-evaluation","Risk-based evaluation",[674,932,933],{},"Match the evaluation rigor to the risk level. A chatbot for memes needs less testing than one making investment decisions.",[687,935,937],{"id":936},"the-future-of-ai-evaluation","The future of AI evaluation",[674,939,940],{},"We may eventually use AI to evaluate AI, especially for scalability and speed. Just make sure the evaluators are trustworthy, too.",[674,942,943,944,949],{},"Expect more guidelines, especially in healthcare, education, and finance. ",[707,945,948],{"href":946,"rel":947},"https:\u002F\u002Fartificialintelligenceact.eu\u002F",[711],"The EU AI Act"," is leading the way in this space.",[674,951,952],{},"Evaluation will become a native part of the dev cycle, not just something you do after training.",[674,954,955],{},"Expect tighter feedback loops and evaluation-driven design.",[674,957,958],{},"The goal of AI evaluation isn’t just to test systems. It’s to build AI you can trust to think, reason, and act in human contexts.",[674,960,961],{},"The better we get at evaluation, the better we’ll be at designing systems that align with real-world values, needs, and expectations.",{"title":963,"searchDepth":964,"depth":964,"links":965},"",2,[966,973,974,979,984,992],{"id":689,"depth":964,"text":690,"children":967},[968,970,971,972],{"id":717,"depth":969,"text":718},3,{"id":736,"depth":969,"text":737},{"id":751,"depth":969,"text":752},{"id":766,"depth":969,"text":767},{"id":773,"depth":964,"text":774},{"id":811,"depth":964,"text":812,"children":975},[976,977,978],{"id":815,"depth":969,"text":816},{"id":822,"depth":969,"text":823},{"id":834,"depth":969,"text":835},{"id":841,"depth":964,"text":842,"children":980},[981,982,983],{"id":845,"depth":969,"text":846},{"id":861,"depth":969,"text":862},{"id":868,"depth":969,"text":869},{"id":881,"depth":964,"text":882,"children":985},[986,987,988,989,990,991],{"id":885,"depth":969,"text":886},{"id":892,"depth":969,"text":893},{"id":908,"depth":969,"text":909},{"id":915,"depth":969,"text":916},{"id":922,"depth":969,"text":923},{"id":929,"depth":969,"text":930},{"id":936,"depth":964,"text":937},"Get started","AI & LLMs","2025-06-17T00:00:00.000Z","AI systems don't behave like traditional software, so they shouldn't be evaluated like it. 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