{"id":28669,"date":"2026-05-22T12:16:00","date_gmt":"2026-05-22T11:16:00","guid":{"rendered":"https:\/\/www.web-systems.pl\/chatbot-on-company-documents-rag-ai-hallucinations\/"},"modified":"2026-05-22T12:16:00","modified_gmt":"2026-05-22T11:16:00","slug":"chatbot-on-company-documents-rag-ai-hallucinations","status":"publish","type":"post","link":"https:\/\/www.web-systems.pl\/en\/chatbot-on-company-documents-rag-ai-hallucinations\/","title":{"rendered":"Chatbot on company documents &#8211; how RAG works and how to avoid AI hallucinations"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Clients ask us about it more and more often: &#8220;Could we have an assistant that knows our documents and answers questions from employees and customers about offers, procedures and contracts?&#8221;. And that is usually how a conversation about a RAG chatbot begins. We at Web Systems, a software house from \u0141\u00f3d\u017a operating since 2006, look at this without illusions. It is not a toy for show. It is a full-blown integration project, with architecture, security and maintenance. In this article I will show how a <strong>chatbot on company documents<\/strong> works, what RAG technology actually is and how to genuinely reduce artificial intelligence hallucinations.<\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_86 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Spis tre\u015bci<\/p>\n<span class=\"ez-toc-title-toggle\"><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.web-systems.pl\/en\/chatbot-on-company-documents-rag-ai-hallucinations\/#What_a_chatbot_on_company_documents_is_and_why_RAG_changes_the_rules_of_the_game\" >What a chatbot on company documents is and why RAG changes the rules of the game<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.web-systems.pl\/en\/chatbot-on-company-documents-rag-ai-hallucinations\/#How_RAG_works_technically_%E2%80%93_from_document_to_answer\" >How RAG works technically &#8211; from document to answer<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.web-systems.pl\/en\/chatbot-on-company-documents-rag-ai-hallucinations\/#Where_AI_hallucinations_come_from_and_how_RAG_limits_them\" >Where AI hallucinations come from and how RAG limits them<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.web-systems.pl\/en\/chatbot-on-company-documents-rag-ai-hallucinations\/#The_most_common_design_mistakes_and_rollout_risks\" >The most common design mistakes and rollout risks<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.web-systems.pl\/en\/chatbot-on-company-documents-rag-ai-hallucinations\/#How_to_build_a_credible_RAG_chatbot_%E2%80%93_practical_guidance\" >How to build a credible RAG chatbot &#8211; practical guidance<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.web-systems.pl\/en\/chatbot-on-company-documents-rag-ai-hallucinations\/#FAQ_%E2%80%93_the_most_common_questions_about_RAG_chatbots_on_company_documents\" >FAQ &#8211; the most common questions about RAG chatbots on company documents<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.web-systems.pl\/en\/chatbot-on-company-documents-rag-ai-hallucinations\/#Does_RAG_completely_eliminate_hallucinations\" >Does RAG completely eliminate hallucinations?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.web-systems.pl\/en\/chatbot-on-company-documents-rag-ai-hallucinations\/#Is_my_data_safe\" >Is my data safe?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.web-systems.pl\/en\/chatbot-on-company-documents-rag-ai-hallucinations\/#Will_the_chatbot_integrate_with_our_systems\" >Will the chatbot integrate with our systems?<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.web-systems.pl\/en\/chatbot-on-company-documents-rag-ai-hallucinations\/#Summary_and_contact\" >Summary and contact<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_a_chatbot_on_company_documents_is_and_why_RAG_changes_the_rules_of_the_game\"><\/span>What a chatbot on company documents is and why RAG changes the rules of the game<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A standard language model answers based on the general knowledge it absorbed during training. The problem is that it does not know your offer, your price list, your complaints policy or your internal onboarding procedure. How could it? Asked about details, it starts guessing. And that is where expensive mistakes are born. A chatbot built on a company&#8217;s internal knowledge base works differently: it bases its answers on real files, not on the model&#8217;s imagination of how your company might operate.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The acronym RAG stands for Retrieval-Augmented Generation, that is, generation enriched with search. The idea is remarkably simple. The model first searches the documents, finds matching fragments, and only then formulates an answer based on them. And thanks to this, artificial intelligence stops being an oracle reciting from memory. It becomes an assistant that reads the source before it says anything. That is a change in the logic of operation from the ground up.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Our clients&#8217; intention is usually clear: limit the AI&#8217;s guesswork and pin the answers to specific procedures, offers, contracts and technical documentation. Nobody wants a flashy gadget that sometimes gets it right and sometimes invents a contract clause. Companies expect a predictable tool that will take some of the calls off the customer service desk and shorten the time employees spend looking for information. And that is it. That is really what it is all about.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As the contractor, we treat such a system as an engineering project. It requires a well-thought-out data processing pipeline, the right choice of vector database, integration with existing B2B systems and access control. A demo cobbled together over a weekend can be impressive in a presentation. But a production rollout that handles hundreds of documents and many users follows completely different rules. That difference only becomes visible once the chatbot starts working on real data and real traffic. Before that, nobody feels it.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"How_RAG_works_technically_%E2%80%93_from_document_to_answer\"><\/span>How RAG works technically &#8211; from document to answer<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Everything starts with ingestion, that is, loading documents into the system. PDF files, DOCX, spreadsheets or database records enter a pipeline where they are parsed and cleaned of disruptive elements such as footers, headers or repeating tables. Then we split the content into smaller fragments called chunks. And the way this split is done matters enormously. Fragments that are too large blur the meaning, ones that are too small lose the context. You have to hit the sweet spot.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We transform each fragment into an embedding, that is, a vector of numbers representing the meaning of the text. These vectors land in a vector database, which enables lightning-fast comparison of semantic similarity. When a user asks a question, the system also turns it into a vector and searches for the fragments lying closest in the space of meanings. This semantic search differs from classic word matching. What counts is the sense of the query, not identical wording.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">At the generation stage, the selected fragments go to the language model as context. The LLM is instructed to answer exclusively on the basis of the material provided, and we call this grounding. Thanks to anchoring the answers in real text, the model minimizes contradictions and stops improvising. The quality of this step depends directly on the accuracy of the earlier search. Garbage in, garbage out.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In simplified terms, the data flow looks as follows:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>A <strong>source document<\/strong> (PDF, DOCX, database) enters the system and is parsed.<\/li>\n<li><strong>Chunking<\/strong> splits the content into semantically coherent fragments.<\/li>\n<li><strong>Embedding<\/strong> turns each fragment into a vector and stores it in the vector database.<\/li>\n<li><strong>Search<\/strong> selects the fragments that best match the user&#8217;s question.<\/li>\n<li>The <strong>answer<\/strong> is produced on the basis of the retrieved context, preserving grounding.<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">The most important element in all of this is retrieval. It is good semantic search over a carefully curated knowledge base that decides whether the model receives the right material. Missed context leads to an answer that is formally correct but completely beside the point. To put it bluntly, the model can beautifully answer a question nobody asked if we feed it the wrong fragments. That is why we treat the investment in retrieval as the heart of the whole system. This is where you win or lose.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Where_AI_hallucinations_come_from_and_how_RAG_limits_them\"><\/span>Where AI hallucinations come from and how RAG limits them<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A hallucination is a situation in which the model invents data absent from the documents or mixes facts from different sources. The result is an answer that sounds credible but is untrue. A non-existent contract clause, a wrong rate, a made-up warranty period. In business, such mistakes can cost dearly, because the user trusts the assistant and acts on its words.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">However, simply plugging in RAG does not solve anything automatically. Hallucinations come back when chunking was done sloppily, when retrieval returns irrelevant fragments, when the data in the database is out of date or when the prompt is so broad that it lets the model give free rein to its imagination. The technology limits the risk but does not remove it. What counts is care at every stage of the pipeline, not the mere fact that a fashionable acronym sits in the architecture.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">There are several mechanisms for limiting it and they work best together. Grounding forces reliance exclusively on the retrieved context. A precise instruction along the lines of &#8220;answer only on the basis of the fragments provided, and if there are none, say that you do not know&#8221; takes away the model&#8217;s permission to guess. On top of that, citing sources with every answer builds trust and makes verification possible.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">And it is precisely citing sources that I consider one of the most effective safeguards I know.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Tip:<\/strong> require the document name and the fragment to be given with every answer. The user can then check the origin of the information with a single click, and the model itself improvises less often, knowing that it has to point to a specific source.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This approach changes the relationship between a person and the assistant. Instead of blindly trusting a generated sentence, the employee sees a footnote leading to the original clause. Something does not add up? Verification takes seconds. A simple mechanism, yet in practice it dramatically raises the credibility of the whole solution and makes it easier to catch any retrieval errors before they reach the end customer.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_most_common_design_mistakes_and_rollout_risks\"><\/span>The most common design mistakes and rollout risks<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Mistake number one, the one we see most often, is dumping raw documents into the system without any preparation. Scans full of noise, tables glued into a single string of characters, zero strategy for splitting into fragments. Retrieval then works on chaos. The effect is easy to predict: the chatbot returns random snippets and the team scratches its head wondering why &#8220;this whole artificial intelligence thing&#8221; does not work. The problem does not sit in the model. It sits in the quality of the input data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The second sin is the lack of evaluation. The team &#8220;feels&#8221; that the system answers sensibly instead of measuring retrieval accuracy and groundedness, that is, the degree to which answers are anchored in the sources. Without metrics you cannot say whether the next change in chunking improved something or broke it. A production rollout without measurement is tuning an engine blindfolded. A straight road to a regression nobody notices until an angry client calls.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Security is a separate topic. Company documents contain sensitive data, so access control is not an add-on but a requirement. The chatbot has to respect permissions. A salesperson should not be able to reach HR documents through the assistant. What is crucial here is the isolation of individual clients&#8217; data and the certainty that fragments from the vector database will not leak to unauthorized recipients. We design this layer from day one instead of taping it on at the end.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Nor can you turn a blind eye to maintenance costs. The knowledge base ages, so a process for updating documents and re-indexing is needed. A growing number of files raises the requirements placed on the vector database, and every query to the model generates inference cost. With heavy traffic, the bill can be quite a surprise if nobody estimated it beforehand.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For us at Web Systems, a rollout comes down to real architectural decisions. We choose the vector database to fit the scale, we weigh an on-premise model against an API depending on data sensitivity and budget, and then we tie the whole thing together with existing B2B systems &#8211; CRM, ERP or document repositories. These choices determine the cost, security and future scalability of the solution. That is why we make them consciously rather than by gut feeling.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"How_to_build_a_credible_RAG_chatbot_%E2%80%93_practical_guidance\"><\/span>How to build a credible RAG chatbot &#8211; practical guidance<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The foundation of a good system is source curation. Before anything reaches the database, the documents have to be cleaned, structured and parsed with their layout in mind. We treat a set of terms and conditions differently than a price table. Only on material prepared this way does it make sense to apply well-thought-out chunking, the choice of an embedding model suited to the Polish language and the rewriting of the user&#8217;s query so that it better matches the content in the database.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Quality measurement is equally important. The approach we call RAG Ops relies on metrics such as groundedness, fluency and answer accuracy. We measure, iterate and improve the search configuration, the chunking strategy and the way questions are rephrased. Such data-driven work lets you gradually climb toward high-quality generation instead of guessing whether a change did anything at all.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Practical guidance we apply when building:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Curation and parsing<\/strong> &#8211; remove the noise, preserve the structure, match the parsing to the document type.<\/li>\n<li><strong>Well-thought-out chunking<\/strong> &#8211; fit the fragment size to the nature of the content, test different variants.<\/li>\n<li><strong>A good embedding model<\/strong> &#8211; pick one that understands the client&#8217;s language and domain well.<\/li>\n<li><strong>Query rewriting<\/strong> &#8211; clean up and clarify the user&#8217;s question before searching.<\/li>\n<li><strong>Measurement and iteration<\/strong> &#8211; treat metrics as a compass, not a one-off report.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Architecture is the foundation of the whole thing. We separate the ingestion, retrieval, generation and user interface layers, keeping a single source of truth for the data and the testability of each component on its own. Such a separation of responsibilities means the system is easier to maintain, scale and develop, and a change in one layer does not turn the others upside down. These are the same engineering principles we have applied for years in <a href=\"https:\/\/www.web-systems.pl\/en\/software-development\/\">custom software development<\/a>, web applications and B2B systems. Nothing new, they simply work.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Tip:<\/strong> start with a well-defined MVP on a narrow set of documents. Measure the results on real questions, fix the weak points, and only then extend the scope of the knowledge base. Trying to cover everything at once ends in a project nobody is able to measure or tame.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"FAQ_%E2%80%93_the_most_common_questions_about_RAG_chatbots_on_company_documents\"><\/span>FAQ &#8211; the most common questions about RAG chatbots on company documents<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Does_RAG_completely_eliminate_hallucinations\"><\/span>Does RAG completely eliminate hallucinations?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">No, but it limits them effectively, provided that retrieval and grounding have been well designed and are measured regularly. RAG anchors the answers in real documents, which makes the model invent things less often. Full elimination does not exist. That said, with careful chunking, accurate search, enforced source citation and constant evaluation, the risk drops to a level that is acceptable for business. The key lies in engineering and maintenance, not in the mere presence of the technology.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Is_my_data_safe\"><\/span>Is my data safe?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">That depends on the architecture adopted, and security can be designed in from the ground up. We use permission-based access control, isolation of individual clients&#8217; data and mechanisms guaranteeing that fragments from the database will not reach unauthorized recipients. For sensitive use cases, deployments closer to the client&#8217;s infrastructure are possible, including on-premise models, which limit sending data outside. We treat security as a design requirement, not an option.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Will_the_chatbot_integrate_with_our_systems\"><\/span>Will the chatbot integrate with our systems?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Yes, and it is integration that is one of the main reasons such projects make sense at all. We connect the chatbot through an API to the documents, databases and B2B tools already running in the company &#8211; from CRM, through ERP, to internal file repositories. Thanks to this, the assistant answers on current data rather than on a static copy from months ago. We define the scope of integration on the basis of the real systems and processes on the client&#8217;s side.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Summary_and_contact\"><\/span>Summary and contact<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">RAG is a way to make a chatbot base its answers on the company&#8217;s real documents instead of guessing on the basis of the model&#8217;s general knowledge. First it searches for the right fragments, then it formulates an answer anchored in the sources. Seemingly a simple change of logic. And yet it radically raises the assistant&#8217;s credibility and turns it into a tool fit for working with contracts, offers and procedures.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The success of such a rollout rests on several pillars: retrieval quality, consistent grounding, quality measurement in the spirit of RAG Ops, data security and a well-thought-out architecture with separated layers. Hallucinations are not removed by magic or by a fashionable acronym on a slide, but by engineering and diligent maintenance. A chatbot that answers flawlessly today will start making mistakes tomorrow if nobody takes care of updating the database and monitoring the metrics. That is simply how it works.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Are you planning a <strong>chatbot on company documents<\/strong>, building an MVP, an integration through an API or broader AI automation in your processes? Let&#8217;s talk about the details. We at Web Systems have been designing web and mobile applications, B2B systems, integrations and <a href=\"https:\/\/www.web-systems.pl\/en\/development-of-artificial-intelligence-based-applications\/\">AI applications for business<\/a> since 2006. We will gladly advise you on how to build a solution that is credible, secure and ready to scale. Get in touch with our team and together we will plan a sensible, technical first step.<\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>Clients ask us about it more and more often: &#8220;Could we have an assistant that knows our documents and answers questions from employees and customers about offers, procedures and contracts?&#8221;. And that is usually how a conversation about a RAG chatbot begins. We at Web Systems, a software house from \u0141\u00f3d\u017a operating since 2006, look [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[116,179,258],"tags":[300,731,704,785,235,659,85],"class_list":["post-28669","post","type-post","status-publish","format-standard","hentry","category-it","category-sztuczna-inteligencja","category-technologie","tag-automatyzacja","tag-baza-wiedzy","tag-chatbot","tag-dokumenty-firmowe","tag-obsluga-klienta","tag-rag","tag-sztuczna-inteligencja"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/posts\/28669","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/comments?post=28669"}],"version-history":[{"count":0,"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/posts\/28669\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/media?parent=28669"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/categories?post=28669"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.web-systems.pl\/en\/wp-json\/wp\/v2\/tags?post=28669"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}