[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"post-how-to-talk-to-a-language-model-prompting-in-practice":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},"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","MightCore","7 min",null,"\u003Cp>The internet is full of lists titled \"10 magic prompts\". They mostly contain phrases like \"you are a world-renowned expert\" and advice to offer the model a tip. None of it works any better than a plain, clear sentence.\u003C\u002Fp>\n\u003Cp>What works is duller: saying exactly what you want.\u003C\u002Fp>\n\n\u003Ch2>Why \"act as an expert\" does nothing\u003C\u002Fh2>\n\u003Cp>There is nobody to fool. A model does not start knowing more because you told it that it knows more. A role can nudge the style of the answer — a more formal register, different vocabulary — but it adds no knowledge.\u003C\u002Fp>\n\u003Cp>The difference you can actually see is between \"write me some copy about our product\" and \"write two paragraphs for a tool retailer, the audience is working tradespeople, emphasise durability and servicing, no superlatives, under 400 characters\".\u003C\u002Fp>\n\u003Cp>The second sentence is not magic. It is just specific.\u003C\u002Fp>\n\n\u003Ch2>Four things that always work\u003C\u002Fh2>\n\u003Cp>\u003Cstrong>1. Context.\u003C\u002Fstrong> The model knows nothing about your company. If you do not say you sell B2B and the reader is a procurement officer, you get copy aimed at a consumer. Not because it erred — because it guessed.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>2. Examples.\u003C\u002Fstrong> The strongest tool you have. Showing two or three finished outputs you like beats a paragraph describing what you want. Models are far better at imitating a pattern than at following an abstract instruction.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>3. Format.\u003C\u002Fstrong> Say what you want back. JSON with these fields. Three bullets. One sentence. Without it you get an essay when you wanted a word.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>4. Boundaries.\u003C\u002Fstrong> What not to do. \"If the text has no due date, return null — do not guess.\" This is your only defence against the model inventing a value it does not have.\u003C\u002Fp>\n\n\u003Ch2>What genuinely helps on harder tasks\u003C\u002Fh2>\n\u003Cp>\"Work through it step by step\" is not an incantation, but it has a real effect where something must be derived. A model that writes the intermediate steps lands the right answer more often than one firing straight at the result. The reason is mundane: each written step becomes part of the context for the next.\u003C\u002Fp>\n\u003Cp>It has a price — you pay for those steps. On simple classification it is waste. On working out whether an invoice matches an order, it earns its keep.\u003C\u002Fp>\n\n\u003Ch2>Where people go wrong\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>\u003Cstrong>The prompt is a list of exceptions.\u003C\u002Fstrong> When it runs three hundred lines and half of them are \"IMPORTANT: don't forget…\", that is not prompt engineering, it is a symptom. Split the task.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Politeness instead of information.\u003C\u002Fstrong> \"Please be so kind as to try…\" is not context. It adds nothing.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Negation.\u003C\u002Fstrong> \"Don't be long-winded\" works worse than \"under 300 characters\". Say what you want, not what you do not.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>One prompt, five jobs.\u003C\u002Fstrong> Read, verify, classify, summarise and reply. Break it up.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Testing on one example.\u003C\u002Fstrong> A prompt that worked once is not a finished prompt. Try it on ten cases, including the ugly ones.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\u003Ch2>A prompt in code is a different discipline\u003C\u002Fh2>\n\u003Cp>People conflate these. A prompt in a chat is written for yourself, and when it returns nonsense you ask again. A prompt in an application runs a thousand times a day with nobody watching.\u003C\u002Fp>\n\u003Cp>Which brings in things a chat never has to handle:\u003C\u002Fp>\n\u003Cp>You are interpolating foreign text — from a user, an email, a document. That text can contain instructions. Separate data from instructions with a clear boundary and tell the model that the content between the markers is data, not a command. It is not bulletproof, but it is the necessary minimum.\u003C\u002Fp>\n\u003Cp>Validate the output. If three JSON fields are expected, check three JSON fields arrived. Without that you are writing a hallucination to your database.\u003C\u002Fp>\n\u003Cp>And version it. A prompt is part of the code — it belongs in git, not in a variable somebody changed once and no longer remembers why.\u003C\u002Fp>\n\n\u003Ch2>The summary\u003C\u002Fh2>\n\u003Cp>A good prompt reads like a good brief for a colleague who knows nothing about your company, is extremely fast, and takes everything literally. No incantations. Just precision.\u003C\u002Fp>\n\u003Cp>If you are writing a prompt and it feels annoyingly specific, you are roughly in the right place.\u003C\u002Fp>","How to write prompts — a practical guide without the incantations","What actually works when writing prompts: context, examples, structured output, and why \"act as an expert\" helps nobody.","2026-07-08T00:00:00.000Z",[16,17,23,29,35,42,49,54,58,64,70,75,80,85,90,95,100,105,111,117,122,127,132,137,142,147,152,157,162,167,172,177,182,187,191,196,201],{"slug":4,"title":5,"excerpt":6,"category":7,"author":8,"readingTime":9,"coverImage":10,"date":14},{"slug":18,"title":19,"excerpt":20,"category":21,"author":8,"readingTime":9,"coverImage":10,"date":22},"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":24,"title":25,"excerpt":26,"category":21,"author":8,"readingTime":27,"coverImage":10,"date":28},"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":30,"title":31,"excerpt":32,"category":33,"author":8,"readingTime":9,"coverImage":10,"date":34},"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":36,"title":37,"excerpt":38,"category":39,"author":8,"readingTime":40,"coverImage":10,"date":41},"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":43,"title":44,"excerpt":45,"category":46,"author":8,"readingTime":47,"coverImage":10,"date":48},"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":50,"title":51,"excerpt":52,"category":39,"author":8,"readingTime":40,"coverImage":10,"date":53},"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":55,"title":56,"excerpt":57,"category":33,"author":8,"readingTime":40,"coverImage":10,"date":53},"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":59,"title":60,"excerpt":61,"category":46,"author":8,"readingTime":62,"coverImage":10,"date":63},"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.","8 min","2026-05-06T00:00:00.000Z",{"slug":65,"title":66,"excerpt":67,"category":68,"author":8,"readingTime":47,"coverImage":10,"date":69},"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":71,"title":72,"excerpt":73,"category":46,"author":8,"readingTime":27,"coverImage":10,"date":74},"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":76,"title":77,"excerpt":78,"category":39,"author":8,"readingTime":47,"coverImage":10,"date":79},"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":81,"title":82,"excerpt":83,"category":7,"author":8,"readingTime":62,"coverImage":10,"date":84},"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":86,"title":87,"excerpt":88,"category":7,"author":8,"readingTime":47,"coverImage":10,"date":89},"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":91,"title":92,"excerpt":93,"category":94,"author":8,"readingTime":47,"coverImage":10,"date":89},"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":96,"title":97,"excerpt":98,"category":7,"author":8,"readingTime":27,"coverImage":10,"date":99},"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":101,"title":102,"excerpt":103,"category":94,"author":8,"readingTime":40,"coverImage":10,"date":104},"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":106,"title":107,"excerpt":108,"category":109,"author":8,"readingTime":62,"coverImage":10,"date":110},"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","2026-02-11T00:00:00.000Z",{"slug":112,"title":113,"excerpt":114,"category":115,"author":8,"readingTime":40,"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":109,"author":8,"readingTime":9,"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":68,"author":8,"readingTime":47,"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":68,"author":8,"readingTime":47,"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":39,"author":8,"readingTime":40,"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":68,"author":8,"readingTime":47,"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":7,"author":8,"readingTime":9,"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":33,"author":8,"readingTime":47,"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":33,"author":8,"readingTime":40,"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":68,"author":8,"readingTime":47,"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":33,"author":8,"readingTime":40,"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":39,"author":8,"readingTime":47,"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":40,"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":94,"author":8,"readingTime":40,"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":7,"author":8,"readingTime":47,"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":68,"author":8,"readingTime":47,"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":40,"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":68,"author":8,"readingTime":9,"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":68,"author":8,"readingTime":47,"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"]