{"id":3556,"date":"2025-10-01T09:08:41","date_gmt":"2025-10-01T08:08:41","guid":{"rendered":"https:\/\/hainzelman.com\/?p=3556"},"modified":"2025-10-15T09:21:00","modified_gmt":"2025-10-15T08:21:00","slug":"why-llms-struggle-with-structured-data-and-what-they-actually-do-behind-the-scenes","status":"publish","type":"post","link":"https:\/\/hainzelman.com\/en\/why-llms-struggle-with-structured-data-and-what-they-actually-do-behind-the-scenes\/","title":{"rendered":"Why LLMs Struggle with Structured Data \u2013 and What They Actually Do Behind the Scenes"},"content":{"rendered":"

Why Structured Data Is Hard for LLMs<\/h2>\n\n\n\n

LLMs are built to predict the next word in a sequence of text. They excel at language, reasoning, and summarization<\/strong>. But structured data (like numbers, tables, and spreadsheets) is fundamentally different:<\/p>\n\n\n\n

    \n
  1. Tokenization Bias<\/strong>
    Numbers are split into text tokens, not treated as real values.\n
      \n
    • Example: The number 12,345 might be broken into tokens like \u201c12\u201d and \u201c,345.\u201d<\/li>\n\n\n\n
    • The model doesn\u2019t \u201cknow\u201d this is twelve-thousand-three-hundred-forty-five.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
    • Lack of Mathematical Precision<\/strong>
      LLMs do not calculate \u2013 they approximate<\/em>.\n
        \n
      • They generate answers based on patterns in their training data.<\/li>\n\n\n\n
      • This is why they sometimes produce \u201challucinated\u201d or flat-out wrong sums, averages, or percentages.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
      • No Native Understanding of Schema<\/strong>
        An Excel sheet or database has rows, columns, and relationships.\n
          \n
        • LLMs don\u2019t inherently understand schema, keys, or data constraints.<\/li>\n\n\n\n
        • Without explicit instructions, they treat tables as chunks of text, not as structured entities.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n
          \n
          <\/div>\n\n\n\n
          <\/div>\n<\/div>\n\n\n\n

          How LLMs \u201cFake It\u201d with Structured Data<\/h2>\n\n\n\n

          When GPT or Gemini appear to handle structured data, they\u2019re often relying on clever workarounds:<\/p>\n\n\n\n

            \n
          • Pattern Matching Instead of Math<\/strong>
            If asked to sum 2 + 2, the model predicts \u201c4\u201d because it has seen this pattern thousands of times in its training. For less common calculations, accuracy drops dramatically.<\/li>\n\n\n\n
          • Internal Tools & Plugins<\/strong>
            Advanced setups route the question to an actual calculator, database, or Python interpreter<\/strong>. The LLM acts as a \u201cfront end\u201d that interprets natural language, sends a query to the right tool, and then reformulates the result in text.<\/li>\n\n\n\n
          • Simulated Table Reasoning<\/strong>
            For tasks like \u201cread a CSV,\u201d the model uses heuristics: it scans the text layout, guesses the relationships, and then outputs answers in natural language. This works for simple tasks but fails at scale.<\/li>\n<\/ul>\n\n\n\n

            Why This Matters for Enterprises<\/h2>\n\n\n\n

            When clients want to start with reporting automation<\/strong>, we explain:<\/p>\n\n\n\n

              \n
            • Reporting involves precision, schema logic, and regulatory reliability<\/strong>.<\/li>\n\n\n\n
            • An LLM alone cannot guarantee correctness.<\/li>\n\n\n\n
            • Errors in compliance or finance reports can be costly.<\/li>\n<\/ul>\n\n\n\n

              That\u2019s why starting with structured data automation is usually the wrong entry point<\/strong>.<\/p>\n\n\n\n

              Where to Start Instead: Unstructured Data<\/h2>\n\n\n\n

              Unstructured data \u2013 documents, emails, manuals, contracts \u2013 is where LLMs shine:<\/p>\n\n\n\n

                \n
              • Summarization<\/li>\n\n\n\n
              • Contextual search (RAG: Retrieval-Augmented Generation)<\/li>\n\n\n\n
              • Q&A over large knowledge bases<\/li>\n\n\n\n
              • Drafting reports from messy inputs<\/li>\n<\/ul>\n\n\n\n

                Here, LLMs provide real value in days, not months<\/strong> \u2013 without requiring perfect numerical precision.<\/p>\n\n\n\n

                Hainzelman\u2019s Approach<\/h2>\n\n\n\n

                At Hainzelman, we recommend:<\/p>\n\n\n\n

                  \n
                1. Start with unstructured data workflows<\/strong> (Knowledge Explorer, Expert Companion).<\/li>\n\n\n\n
                2. Introduce connectors<\/strong> for structured data sources, but keep precision-critical tasks in traditional systems.<\/li>\n\n\n\n
                3. Combine strengths<\/strong>: let LLMs interpret natural language, while reliable engines handle the math and schema operations.<\/li>\n<\/ol>\n\n\n\n

                  This hybrid approach ensures trustworthy results, measurable ROI, and compliance<\/strong> \u2013 without overloading LLMs with tasks they\u2019re not built to handle.<\/p>","protected":false},"excerpt":{"rendered":"

                  Why Structured Data Is Hard for LLMs LLMs are built to predict the next word in a sequence of text. They excel at language, reasoning, and summarization. But structured data (like numbers, tables, and spreadsheets) is fundamentally different: How LLMs \u201cFake It\u201d with Structured Data When GPT or Gemini appear to handle structured data, they\u2019re […]<\/p>","protected":false},"author":7,"featured_media":3987,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[],"post_folder":[],"class_list":["post-3556","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-nicht-kategorisiert"],"acpt":{"meta":[{"meta_box":"artikelinfos","meta_fields":[{"name":"introtext","type":"Editor","options":[],"value":"

                  At Hainzelman, we regularly meet clients who want to start their AI journey with reporting and numbers<\/strong>.
                  The logic sounds simple: \u201cIf ChatGPT can solve math problems in a chat window, why shouldn\u2019t it also automate our reporting workflows?\u201d<\/p>\r\n

                  But here\u2019s the catch: large language models (LLMs) like GPT or Gemini are not designed for structured data processing<\/strong>. Let\u2019s unpack why this is more complicated than it looks \u2013 and what\u2019s really happening when LLMs seem to \u201cdo numbers.\u201d<\/p>","default":"","required":false,"showInAdmin":false,"advancedOptions":{"1":{"id":"635da7e3-16c5-4c45-9396-8f8cc5022c22","boxId":"4c3b8b56-bc00-4175-866b-0e2bc637cc85","fieldId":"b38c687f-a615-4a96-9ddd-a6334f7464c1","key":"headline","value":"top"},"2":{"id":"7061f47e-635e-4947-81c0-32a05dd8f7e4","boxId":"4c3b8b56-bc00-4175-866b-0e2bc637cc85","fieldId":"b38c687f-a615-4a96-9ddd-a6334f7464c1","key":"width","value":""},"6":{"id":"7ab3846c-83c9-4fe9-9eca-717847d065e9","boxId":"4c3b8b56-bc00-4175-866b-0e2bc637cc85","fieldId":"b38c687f-a615-4a96-9ddd-a6334f7464c1","key":"before","value":""},"7":{"id":"eeec215d-3a80-4129-a362-1a3ed0ee9d01","boxId":"4c3b8b56-bc00-4175-866b-0e2bc637cc85","fieldId":"b38c687f-a615-4a96-9ddd-a6334f7464c1","key":"after","value":""},"8":{"id":"00e6dd1a-b549-434a-a28f-161a822f4883","boxId":"4c3b8b56-bc00-4175-866b-0e2bc637cc85","fieldId":"b38c687f-a615-4a96-9ddd-a6334f7464c1","key":"min","value":""},"9":{"id":"a8e88967-aaa6-4508-95a2-13b801c6e648","boxId":"4c3b8b56-bc00-4175-866b-0e2bc637cc85","fieldId":"b38c687f-a615-4a96-9ddd-a6334f7464c1","key":"max","value":""},"18":{"id":"c0defcc3-1764-4e62-9d9f-b1473eecc596","boxId":"4c3b8b56-bc00-4175-866b-0e2bc637cc85","fieldId":"b38c687f-a615-4a96-9ddd-a6334f7464c1","key":"css","value":""},"25":{"id":"f7e0a6fa-b0a8-4480-8ca7-d3f78d1c0508","boxId":"4c3b8b56-bc00-4175-866b-0e2bc637cc85","fieldId":"b38c687f-a615-4a96-9ddd-a6334f7464c1","key":"cols","value":""},"26":{"id":"5da06f9c-573e-4be7-8adc-d3c4b8b7def1","boxId":"4c3b8b56-bc00-4175-866b-0e2bc637cc85","fieldId":"b38c687f-a615-4a96-9ddd-a6334f7464c1","key":"rows","value":""},"29":{"id":"0b081d29-62bf-487f-ab36-e2a8d669b8e4","boxId":"4c3b8b56-bc00-4175-866b-0e2bc637cc85","fieldId":"b38c687f-a615-4a96-9ddd-a6334f7464c1","key":"vertical_alignment","value":"center"},"38":{"id":"5ce1708d-aa0f-4d69-a723-6a645d0c64e2","boxId":"4c3b8b56-bc00-4175-866b-0e2bc637cc85","fieldId":"b38c687f-a615-4a96-9ddd-a6334f7464c1","key":"allow_html","value":"1"}}},{"name":"key-takeaways-2","type":"Repeater","options":[],"value":{"list-icon":[{"original_name":"list-icon","type":"HTML","value":""},{"original_name":"list-icon","type":"HTML","value":""},{"original_name":"list-icon","type":"HTML","value":""}],"summary-item-text":[{"original_name":"summary-item-text","type":"Editor","value":"

                  If you think your AI journey should begin with Excel reporting or finance dashboards, think again.<\/p>"},{"original_name":"summary-item-text","type":"Editor","value":"

                  Start where LLMs are strong \u2013 unstructured data \u2013 and expand from there with the right architecture. That\u2019s the Hainzelman way.<\/p>"},{"original_name":"summary-item-text","type":"Editor","value":"

                  Want to learn more? Book a demo and see how we help enterprises use AI where it makes the most impact.<\/p>"}]},"default":"","required":false,"showInAdmin":false,"advancedOptions":[]}]}]},"acf":[],"_links":{"self":[{"href":"https:\/\/hainzelman.com\/en\/wp-json\/wp\/v2\/posts\/3556","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hainzelman.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hainzelman.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hainzelman.com\/en\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/hainzelman.com\/en\/wp-json\/wp\/v2\/comments?post=3556"}],"version-history":[{"count":28,"href":"https:\/\/hainzelman.com\/en\/wp-json\/wp\/v2\/posts\/3556\/revisions"}],"predecessor-version":[{"id":3998,"href":"https:\/\/hainzelman.com\/en\/wp-json\/wp\/v2\/posts\/3556\/revisions\/3998"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/hainzelman.com\/en\/wp-json\/wp\/v2\/media\/3987"}],"wp:attachment":[{"href":"https:\/\/hainzelman.com\/en\/wp-json\/wp\/v2\/media?parent=3556"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hainzelman.com\/en\/wp-json\/wp\/v2\/categories?post=3556"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hainzelman.com\/en\/wp-json\/wp\/v2\/tags?post=3556"},{"taxonomy":"post_folder","embeddable":true,"href":"https:\/\/hainzelman.com\/en\/wp-json\/wp\/v2\/post_folder?post=3556"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}