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AI systems prefer clear, consistent facts over industry- and company-specific brand language.","number",[],{"_key":893,"_type":164,"children":894,"level":303,"listItem":890,"markDefs":903,"style":172},"605f7a1cf3db",[895,899],{"_key":896,"_type":168,"marks":897,"text":898},"931adf9367d0",[738],"Answer formats instead of continuous text: ",{"_key":900,"_type":168,"marks":901,"text":902},"e1a68d43b087",[],"Tests with users often yield clearly formulated question-and-answer pairs, glossaries, and step-by-step explanations—exactly the formats that generative search systems prefer to cite.",[],{"_key":905,"_type":164,"children":906,"level":303,"listItem":890,"markDefs":915,"style":172},"07b5d17b2e4a",[907,911],{"_key":908,"_type":168,"marks":909,"text":910},"5f97c05a54db",[738],"Verified facts instead of claims: ",{"_key":912,"_type":168,"marks":913,"text":914},"7ec93cabc5b1",[],"When an evaluation reveals gaps, the content is refined and backed up with sources. This increases the likelihood of being cited in a generated response.",[],{"_key":917,"_type":164,"children":918,"markDefs":923,"style":172},"10e285007b0c",[919],{"_key":920,"_type":168,"marks":921,"text":922},"a205e287d9bf",[],"When the search shifts to the response section of AI systems, users receive an answer there instead of a list of links. Whether they go on to visit a website afterward depends on whether it is cited as a source in the answer. It is crucial that the content be clear, up-to-date, and consistent.",[],{"_key":925,"_type":164,"children":926,"markDefs":931,"style":172},"d6e95a7c750e",[927],{"_key":928,"_type":168,"marks":929,"text":930},"8ffbddb6257c",[],"Against this backdrop, FAQs, glossaries, and step-by-step explanations are more than just good editorial practice. When they result from a thorough chatbot evaluation, they help companies stand out in AI search results.",[],{"_key":933,"_type":164,"children":934,"markDefs":939,"style":172},"b356d9495aa4",[935],{"_key":936,"_type":168,"marks":937,"text":938},"74bc16536d8b",[738],"Conclusion: Plan for evaluation early on",[],{"_key":941,"_type":164,"children":942,"markDefs":947,"style":172},"656760944d54",[943],{"_key":944,"_type":168,"marks":945,"text":946},"16758c996c0a",[],"A chatbot developed in-house offers more than just independence from the feature set of an off-the-shelf solution. It allows you to systematically test, learn from the results, and continuously improve your own content—with benefits that extend beyond the chatbot itself. Evaluation is often underestimated as a mere quality check right before launch. 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The system understands context, provides relevant answers on its own, and handles complex inquiries. Just a few days after its launch, several thousand conversations had already taken place via the AI chatbot—which was fully operational from day one.\nIn the future, this new service offering is intended to reduce the workload on customer service representatives and help them prioritize inquiries more efficiently and effectively. With this solution, BARMER now has both the technical platform and the in-house expertise to independently further develop its AI-based services.",[],"The digital advisory assistant for barmer.de – always available, with up-to-date information",{"_key":1060,"_type":214,"headline":1061,"sanityImage":1062,"text":1065},"5d6fe6fb3631","Precise answers, clear boundaries",{"_type":220,"aspectRatioXs":247,"asset":1063},{"_ref":1064,"_type":225},"image-7026db7b3c7440f6da97c78803f703457a24f12d-1560x1560-jpg",[1066,1074],{"_key":1067,"_type":164,"children":1068,"markDefs":1073,"style":172},"2a621c629d2a",[1069],{"_key":1070,"_type":168,"marks":1071,"text":1072},"5f1a97b432cd",[],"The BARMER AI chatbot answers questions about health and long-term care insurance, health benefits, membership, and many other topics related to the health insurance provider—directly on barmer.de, around the clock. 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The user interface guides users precisely to the right point of contact.",[],{"_key":1083,"_type":214,"headline":1084,"sanityImage":1085,"text":1088},"69f33c5e94bc","From intent logic to agent-based decision-making",{"_type":220,"aspectRatioXs":247,"asset":1086},{"_ref":1087,"_type":225},"image-3c5fa1e329012c3824fce9462ca844e6c4281b3e-1560x1560-jpg",[1089,1097],{"_key":1090,"_type":164,"children":1091,"markDefs":1096,"style":172},"4a3b380d4e31",[1092],{"_key":1093,"_type":168,"marks":1094,"text":1095},"6fda0dd7234c",[],"The original plan was an intent-based chatbot: predefined paths, manually maintained response structures, and clear routing logic. It quickly turned our, however, that this model is not viable for the scope of knowledge required by a large health insurance provider: too many topics, too many variations, and too much maintenance effort. 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This intrinsic urge to explore helps me see the opportunities in new things, even if it means investing up front. At BARMER.KI, the mission has two sides: trying out new technology and finding a way to anchor it in the organization. That takes someone who isn’t just convinced by the technology but also gets that conviction across internally. I know the business from customer service all the way to the legacy systems, and that builds trust. 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Today, I can vibe a vision into shape with AI as my sparring partner and make it visible and tangible within a few hours.",[],{"_key":1434,"_type":164,"children":1435,"markDefs":1440,"style":172},"a75cdc6194be",[1436],{"_key":1437,"_type":168,"marks":1438,"text":1439},"c9f8d15b7e86",[738],"Has vibe coding changed the way you conceive and prioritize products?",[],{"_key":1442,"_type":164,"children":1443,"markDefs":1448,"style":172},"87ce115a68a7",[1444],{"_key":1445,"_type":168,"marks":1446,"text":1447},"763d7f956ddc",[],"The question of whether an idea or a project can work can be answered much faster. Vibe coding keeps the barrier to simply trying things out to a minimum. In my previous work, there were often long discussions about whether something was even feasible. By nature, I’m someone who only believes “that won’t work” once I’ve failed at it myself. One example: For a current project, I had to check whether an optical character recognition (OCR) workflow for analyzing receipts was technically feasible. Over a weekend, I set up a private server, used n8n and Claude, and tested the core process. It worked. 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