{"id":1906,"date":"2026-06-07T15:00:11","date_gmt":"2026-06-07T15:00:11","guid":{"rendered":"https:\/\/united-in-pace.com\/?p=1906"},"modified":"2026-06-07T15:10:53","modified_gmt":"2026-06-07T15:10:53","slug":"coacheval-a-benchmark-approach-for-evaluating-ai-model-performances-on-agentic-coaching-in-endurance-sports","status":"publish","type":"post","link":"https:\/\/united-in-pace.com\/de\/coacheval-a-benchmark-approach-for-evaluating-ai-model-performances-on-agentic-coaching-in-endurance-sports\/","title":{"rendered":"CoachEval &#8211; Evaluating AI model performances on agentic coaching in endurance sports."},"content":{"rendered":"<p>As part of developing my app for AI-powered training planning and coaching in endurance sports, I\u2019ve been spending a lot of time lately looking into LLM benchmarks and setting up my own evaluations. What data do I feed into the LLM? How does it respond in specific situations? What tools do I provide to the LLM? Are the training plans the LLM generates good and effective? This is a very exciting task. This week, Strava launched its own MCP server. So many athletes will use this to evaluate their training as well as to create training plans. I think benchmarks are becoming increasingly important for improving the quality of LLMs in order to improve the coaching skills.<\/p>\n\n\n\n<p>The idea: <strong>CoachEval<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"887\" height=\"933\" src=\"https:\/\/i0.wp.com\/united-in-pace.com\/wp-content\/uploads\/2026\/06\/Bildschirmfoto-2026-06-07-um-16.20.32.png?resize=887%2C933&#038;ssl=1\" alt=\"\" class=\"wp-image-1909\" srcset=\"https:\/\/i0.wp.com\/united-in-pace.com\/wp-content\/uploads\/2026\/06\/Bildschirmfoto-2026-06-07-um-16.20.32.png?w=887&amp;ssl=1 887w, https:\/\/i0.wp.com\/united-in-pace.com\/wp-content\/uploads\/2026\/06\/Bildschirmfoto-2026-06-07-um-16.20.32.png?resize=285%2C300&amp;ssl=1 285w, https:\/\/i0.wp.com\/united-in-pace.com\/wp-content\/uploads\/2026\/06\/Bildschirmfoto-2026-06-07-um-16.20.32.png?resize=768%2C808&amp;ssl=1 768w, https:\/\/i0.wp.com\/united-in-pace.com\/wp-content\/uploads\/2026\/06\/Bildschirmfoto-2026-06-07-um-16.20.32.png?resize=11%2C12&amp;ssl=1 11w\" sizes=\"auto, (max-width: 887px) 100vw, 887px\" \/><\/figure>\n\n\n\n<p>Stay tuned!<\/p>\n\n\n\n<p>Interested in what I&#8217;m doing? Contact me at <a href=\"mailto:mail@united-in-pace.com\">mail@united-in-pace.com<\/a><\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-a89b3969 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\">Work with me<\/a><\/div>\n<\/div>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>As part of developing my app for AI-powered training planning and coaching in endurance sports, I\u2019ve been spending a lot of time lately looking into LLM benchmarks and setting up my own evaluations. What data do I feed into the LLM? How does it respond in specific situations? What tools do I provide to the [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1923,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_wp_convertkit_post_meta":{"form":"-1","landing_page":"0","tag":"0","restrict_content":"0"},"footnotes":""},"categories":[19],"tags":[],"class_list":["post-1906","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-lab"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>CoachEval - Evaluating AI model performances on agentic coaching in endurance sports. | UNITED IN PACE<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/united-in-pace.com\/de\/coacheval-a-benchmark-approach-for-evaluating-ai-model-performances-on-agentic-coaching-in-endurance-sports\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"CoachEval - Evaluating AI model performances on agentic coaching in endurance sports. | UNITED IN PACE\" \/>\n<meta property=\"og:description\" content=\"As part of developing my app for AI-powered training planning and coaching in endurance sports, I\u2019ve been spending a lot of time lately looking into LLM benchmarks and setting up my own evaluations. 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