[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"post-what-is-an-llm-large-language-model-explained":3,"$fXeC77ibo_YuG-ZnFG3hwyaNC83zFAKw-FvBNoL2JrIM":15},{"slug":4,"title":5,"excerpt":6,"category":7,"author":8,"readingTime":9,"coverImage":10,"bodyHtml":11,"metaTitle":12,"metaDescription":13,"date":14},"what-is-an-llm-large-language-model-explained","What an LLM is: large language models without the mystique","How does a program that predicts the next word end up writing working code? A look at what actually happens inside a language model.","Fundamentals","MightCore","8 min",null,"\u003Cp>The explanation you hear most often is: a large language model predicts the next word. That is true and also unhelpful. Your phone's autocomplete predicts the next word too, and nobody expects it to refactor their code.\u003C\u002Fp>\n\u003Cp>The difference is in everything the model had to learn in order to get that word right.\u003C\u002Fp>\n\n\u003Ch2>Prediction as a side effect\u003C\u002Fh2>\n\u003Cp>Imagine your only job is finishing the last word of a sentence. For \"The coffee is hot, so I'll wait for it to ___\" a feel for language is enough. For \"The sum of 47 and 58 is ___\" you now have to do arithmetic. For \"This function crashes because the variable is ___\" you have to understand code.\u003C\u002Fp>\n\u003Cp>That is the whole trick. Train a model to complete text across the entire internet — books, documentation, forums, source code — and it cannot do the job without picking up grammar, arithmetic, a chunk of world knowledge and the structure of programming languages along the way. The abilities are not the goal. They are a by-product of one dumb-sounding task, repeated at enormous scale.\u003C\u002Fp>\n\u003Cp>The \"large\" in LLM does not mean a big file. It means billions of parameters — numbers tuned during training. Everything the model learned lives in them.\u003C\u002Fp>\n\n\u003Ch2>Tokens: the model does not see words\u003C\u002Fh2>\n\u003Cp>Text is chopped into tokens before processing. A token is roughly a piece of a word — common words tend to be one token, longer or unusual ones break into several.\u003C\u002Fp>\n\u003Cp>It sounds like trivia, but it explains oddities you will hit. Why does the model sometimes miscount letters in a word? Because it never sees letters, it sees tokens. Why do APIs bill per token rather than per character? Because tokens are the unit the model actually works in. And why does Slovak or Czech burn more tokens than English for the same sentence? Because the tokeniser was trained mostly on English.\u003C\u002Fp>\n\n\u003Ch2>The context window is a desk, not a memory\u003C\u002Fh2>\n\u003Cp>The context window is how many tokens the model can hold at once — your question, the conversation so far, attached documents, system instructions. All of it together.\u003C\u002Fp>\n\u003Cp>The important part: the model remembers \u003Cstrong>nothing\u003C\u002Fstrong> between calls. When it seems to recall what you said five minutes ago, that is because the application resent the entire conversation. Every single time. The model is a colleague with perfect amnesia: you hand over the whole folder each time and they read it from scratch.\u003C\u002Fp>\n\u003Cp>Two consequences follow. Long conversations are expensive, because the whole thing gets resent. And when the window overflows, something falls out — usually the oldest part. That is why long chats occasionally act as though they forgot what you agreed. They did not forget. They never saw it.\u003C\u002Fp>\n\n\u003Ch2>Temperature, and why you get a different answer twice\u003C\u002Fh2>\n\u003Cp>The model does not pick one next word. It computes a probability across the whole vocabulary and samples from it. The temperature setting controls how far it may stray from the most likely choice.\u003C\u002Fp>\n\u003Cp>Low temperature means predictable, dull, consistent. High means creative, surprising, unreliable. For pulling fields off an invoice you want it low. For brainstorming campaign names, higher. There is no correct value, only a correct value for the task.\u003C\u002Fp>\n\u003Cp>This also explains why testing AI features is tedious. The same input gives you a slightly different answer every time.\u003C\u002Fp>\n\n\u003Ch2>Hallucination is not a bug\u003C\u002Fh2>\n\u003Cp>The model never distinguished between \"I know this\" and \"this sounds plausible\". It is doing the same thing throughout: finding the most natural continuation of the text. Ask about a fact that was not in the training data and it will not say \"I don't know\" — because the natural continuation of a question is an answer, not an admission of ignorance.\u003C\u002Fp>\n\u003Cp>So it will confidently invent a standard that does not exist, cite the wrong clause, or recommend a book nobody wrote. This is not lying. It is statistics doing exactly what it was built to do.\u003C\u002Fp>\n\u003Cp>You can reduce it — give the model real source material and have it answer from that (the idea behind RAG), demand citations, verify output with code. You cannot remove it.\u003C\u002Fp>\n\n\u003Ch2>Why this matters\u003C\u002Fh2>\n\u003Cp>Most disappointment with LLMs comes from expecting a database and getting a language model. A database returns a fact or an error. A language model always returns text that reads well.\u003C\u002Fp>\n\u003Cp>Once that lands, the rest follows: why context matters, why blind trust is a mistake, and why the best AI features look boring — the model proposes, a human confirms.\u003C\u002Fp>","What is an LLM — large language models explained","How a large language model works, what tokens, context windows and temperature mean, and why models hallucinate.","2026-02-11T00:00:00.000Z",[16,23,29,35,41,48,55,60,64,69,75,80,85,90,95,100,105,110,111,117,122,127,132,137,142,147,152,157,162,167,172,177,182,187,191,196,201],{"slug":17,"title":18,"excerpt":19,"category":20,"author":8,"readingTime":21,"coverImage":10,"date":22},"how-to-talk-to-a-language-model-prompting-in-practice","How to talk to a model: prompting without incantations","Prompt engineering is not a list of magic phrases. It is the ability to say exactly what you want — which is harder than it sounds.","Guides","7 min","2026-07-08T00:00:00.000Z",{"slug":24,"title":25,"excerpt":26,"category":27,"author":8,"readingTime":21,"coverImage":10,"date":28},"how-to-choose-a-language-model-and-control-costs","Choosing a language model and keeping costs under control","Benchmarks tell you almost nothing useful. What actually decides a model choice, and where the costs nobody predicted come from.","Engineering","2026-06-30T00:00:00.000Z",{"slug":30,"title":31,"excerpt":32,"category":27,"author":8,"readingTime":33,"coverImage":10,"date":34},"programming-in-the-age-of-ai-what-actually-changed","Programming in the age of AI: what actually changed","No, it did not replace developers. But it changed where they spend their time — and not all of those changes are comfortable.","9 min","2026-06-17T00:00:00.000Z",{"slug":36,"title":37,"excerpt":38,"category":39,"author":8,"readingTime":21,"coverImage":10,"date":40},"ai-modelka-pre-vas-brand-sprievodca","An AI model for your brand: the complete guide","From brief through avatar creation to the first campaign — step by step.","AI UGC","2026-06-16T00:00:00.000Z",{"slug":42,"title":43,"excerpt":44,"category":45,"author":8,"readingTime":46,"coverImage":10,"date":47},"pripadova-studia-cleago","Case study: Cleago — a platform built on context","How we designed and built a solution for Cleago (www.cleago.sk) by first understanding the context and only then coding.","Case studies","5 min","2026-06-02T00:00:00.000Z",{"slug":49,"title":50,"excerpt":51,"category":52,"author":8,"readingTime":53,"coverImage":10,"date":54},"where-not-to-use-ai-and-why-nobody-tells-you","Where not to use AI (and why nobody tells you)","A company selling AI has little incentive to talk about its limits. Let us fix that — these are the places AI simply does not belong.","For business","6 min","2026-05-27T00:00:00.000Z",{"slug":56,"title":57,"excerpt":58,"category":45,"author":8,"readingTime":46,"coverImage":10,"date":59},"pripadova-studia-produktove-fotky","Case study: 80% less time spent creating product photos","A real example of deploying AI photos in an online store — from brief to results.","2026-05-19T00:00:00.000Z",{"slug":61,"title":62,"excerpt":63,"category":39,"author":8,"readingTime":46,"coverImage":10,"date":59},"ai-model-consistent-brand-across-campaigns","A consistent brand with an AI model across campaigns","An AI model can be a brand's steady face — if you handle consistency and transparency the right way. Here is how.",{"slug":65,"title":66,"excerpt":67,"category":52,"author":8,"readingTime":9,"coverImage":10,"date":68},"where-ai-actually-fits-in-a-company","Where AI actually fits in a company (and where it is just flashy)","Concrete applications across departments — what works today, what needs preparation, and what is still a demo rather than a tool.","2026-05-06T00:00:00.000Z",{"slug":70,"title":71,"excerpt":72,"category":73,"author":8,"readingTime":53,"coverImage":10,"date":74},"rest-vs-graphql-pre-eshopy","REST vs. GraphQL API for modern online stores","When to choose which approach and what the impact on performance and development is.","Development","2026-04-21T00:00:00.000Z",{"slug":76,"title":77,"excerpt":78,"category":52,"author":8,"readingTime":33,"coverImage":10,"date":79},"how-to-bring-ai-into-your-company-first-steps","How to bring AI into your company without wasting the money","Most AI projects do not fail on technology. They fail because nobody said what was supposed to get better. Where to start instead.","2026-04-15T00:00:00.000Z",{"slug":81,"title":82,"excerpt":83,"category":45,"author":8,"readingTime":53,"coverImage":10,"date":84},"case-study-monolith-to-modular-migration","Case study: from a monolith to a modular architecture with no downtime","An illustrative example of gradually modernising an older application — where every change was risky and maintenance expensive.","2026-04-14T00:00:00.000Z",{"slug":86,"title":87,"excerpt":88,"category":20,"author":8,"readingTime":9,"coverImage":10,"date":89},"ai-agents-and-tool-calling-when-it-makes-sense","AI agents and tool calling: when it makes sense and when it does not","An agent is a model allowed to act. That is interesting and dangerous in equal measure. How it works, and where to be careful.","2026-03-25T00:00:00.000Z",{"slug":91,"title":92,"excerpt":93,"category":20,"author":8,"readingTime":53,"coverImage":10,"date":94},"context-driven-development-context-gathering-in-practice","Context Driven Development in practice: how gathering context changes the outcome","The most expensive mistakes come from a misunderstood brief. Here is what the context gathering that prevents them looks like.","2026-03-17T00:00:00.000Z",{"slug":96,"title":97,"excerpt":98,"category":99,"author":8,"readingTime":53,"coverImage":10,"date":94},"uctovnictvo-novej-generacie","Next-generation accounting: a platform built on context","A vision of an intelligent layer on top of existing accounting tools.","Accounting",{"slug":101,"title":102,"excerpt":103,"category":20,"author":8,"readingTime":33,"coverImage":10,"date":104},"how-to-orchestrate-language-models-in-practice","Orchestrating language models: from one prompt to a system","One prompt is a demo. An application is something else. On splitting work, routing between models, and where to leave ordinary code alone.","2026-03-04T00:00:00.000Z",{"slug":106,"title":107,"excerpt":108,"category":99,"author":8,"readingTime":46,"coverImage":10,"date":109},"digitalising-accounting-e-invoicing","Digitalising accounting: e-invoicing and what it brings","Electronic invoicing and reporting are becoming the standard. What it means for businesses and how to prepare without panic.","2026-02-17T00:00:00.000Z",{"slug":4,"title":5,"excerpt":6,"category":7,"author":8,"readingTime":9,"coverImage":10,"date":14},{"slug":112,"title":113,"excerpt":114,"category":115,"author":8,"readingTime":46,"coverImage":10,"date":116},"ako-ai-setri-naklady-na-video","How AI cuts the cost of video content production","Concrete numbers and a workflow for creating AI videos for online stores.","Marketing","2026-02-10T00:00:00.000Z",{"slug":118,"title":119,"excerpt":120,"category":7,"author":8,"readingTime":21,"coverImage":10,"date":121},"what-is-artificial-intelligence-explained-without-the-marketing","What artificial intelligence is (and what it is not)","The word AI now means everything, and therefore nothing. Here is what is actually behind it, and where technology ends and marketing begins.","2026-01-21T00:00:00.000Z",{"slug":123,"title":124,"excerpt":125,"category":73,"author":8,"readingTime":53,"coverImage":10,"date":126},"vector-databases-and-embeddings","Vector databases and embeddings: how machines grasp meaning","Semantic search sits behind many AI features. Here is what embeddings are and why modern data work rests on them.","2026-01-20T00:00:00.000Z",{"slug":128,"title":129,"excerpt":130,"category":73,"author":8,"readingTime":53,"coverImage":10,"date":131},"trendy-v-ai-vyvoji-2026","Trends in AI development for 2026","What awaits companies in the area of AI agents, automation and infrastructure.","2026-01-14T00:00:00.000Z",{"slug":133,"title":134,"excerpt":135,"category":45,"author":8,"readingTime":46,"coverImage":10,"date":136},"case-study-ai-product-photography-cosmetics","Case study: AI product photography for a cosmetics e-shop","An illustrative example of how AI content replaced repeated photoshoots and brought a consistent visual identity across seasons.","2025-12-09T00:00:00.000Z",{"slug":138,"title":139,"excerpt":140,"category":73,"author":8,"readingTime":53,"coverImage":10,"date":141},"integrating-ai-into-existing-systems","How to integrate AI into existing systems without a rewrite","You do not have to throw away working software to use AI. Here is an approach that adds value step by step, with low risk.","2025-11-18T00:00:00.000Z",{"slug":143,"title":144,"excerpt":145,"category":20,"author":8,"readingTime":21,"coverImage":10,"date":146},"gdpr-a-ai-obsah","GDPR and AI content: what to watch out for","The legal minimum for companies working with AI content and personal data.","2025-11-11T00:00:00.000Z",{"slug":148,"title":149,"excerpt":150,"category":39,"author":8,"readingTime":53,"coverImage":10,"date":151},"virtualne-ai-modelky","Virtual AI models: the future of advertising or a passing trend?","The possibilities, limits and ethics of virtual influencers for brands.","2025-10-20T00:00:00.000Z",{"slug":153,"title":154,"excerpt":155,"category":39,"author":8,"readingTime":46,"coverImage":10,"date":156},"ai-ugc-in-performance-marketing","AI UGC in performance marketing: what works and what applies","How to use AI content in Meta and TikTok campaigns, why creative testing matters, and what AI-labelling rules apply.","2025-10-14T00:00:00.000Z",{"slug":158,"title":159,"excerpt":160,"category":73,"author":8,"readingTime":53,"coverImage":10,"date":161},"llm-hallucinations-how-to-limit-them","LLM hallucinations and how to limit them in practice","Why AI sometimes states nonsense with confidence, and the techniques we use to keep output trustworthy.","2025-09-16T00:00:00.000Z",{"slug":163,"title":164,"excerpt":165,"category":39,"author":8,"readingTime":46,"coverImage":10,"date":166},"co-je-ai-ugc","What AI UGC is and why the whole world is talking about it","An introduction to AI-generated UGC and its impact on advertising and customer trust.","2025-09-15T00:00:00.000Z",{"slug":168,"title":169,"excerpt":170,"category":45,"author":8,"readingTime":53,"coverImage":10,"date":171},"case-study-b2b-eshop-faster-delivery","Case study: a B2B e-shop ready in weeks, not months","An illustrative example of how context gathering and AI execution shortened a wholesale e-shop build — without cutting quality.","2025-08-19T00:00:00.000Z",{"slug":173,"title":174,"excerpt":175,"category":115,"author":8,"readingTime":46,"coverImage":10,"date":176},"ai-v-marketingu-od-experimentu-k-vysledkom","AI in marketing: from experiment to real results","How to move from \"playing with AI\" to a measurable return on investment.","2025-08-06T00:00:00.000Z",{"slug":178,"title":179,"excerpt":180,"category":99,"author":8,"readingTime":46,"coverImage":10,"date":181},"ai-invoice-processing-in-accounting","AI invoice processing: from scan to posting","Intelligent document processing cuts the routine of retyping invoices. How it works and where AI has its limits.","2025-07-15T00:00:00.000Z",{"slug":183,"title":184,"excerpt":185,"category":20,"author":8,"readingTime":53,"coverImage":10,"date":186},"rag-why-context-decides-ai-quality","RAG: why context decides the quality of AI output","Retrieval-Augmented Generation connects a language model to your own data. Here is how it works and when to use it.","2025-06-18T00:00:00.000Z",{"slug":188,"title":189,"excerpt":190,"category":73,"author":8,"readingTime":53,"coverImage":10,"date":186},"shopsys-vs-vlastne-riesenie","ShopSys vs. a custom build: when a framework pays off","A decision framework for e-shop owners facing the choice of platform.",{"slug":192,"title":193,"excerpt":194,"category":115,"author":8,"readingTime":46,"coverImage":10,"date":195},"ako-ai-meni-ecommerce-na-slovensku","How AI is changing e-commerce","Practical examples of AI in product content, search and personalisation for online stores.","2025-05-21T00:00:00.000Z",{"slug":197,"title":198,"excerpt":199,"category":73,"author":8,"readingTime":21,"coverImage":10,"date":200},"context-driven-development-novy-pristup","Context Driven Development: a new approach to building software","An explanation of the CDD methodology from gathering context to deployment — step by step.","2025-04-09T00:00:00.000Z",{"slug":202,"title":203,"excerpt":204,"category":73,"author":8,"readingTime":53,"coverImage":10,"date":205},"koniec-ery-predrazeneho-vyvoja","Why the era of overpriced software development is over","How AI and a context-driven approach are changing the economics of building software — and why paying for inflated hours no longer makes sense.","2025-03-12T00:00:00.000Z"]