Educational writing on AI, large language models, RAG and Turkish NLP.
A new class of models does not answer right away; it 'thinks' first. Here is what that thinking really is, why it solves certain problems, what it costs, and why you do not need it for every task — without hiding behind jargon.
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On its own, an AI model cannot check your calendar, query your database, or read a file. MCP is the common, open standard for connecting models to external tools and data — the USB-C of AI.
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Getting good results is no longer a single 'right question'; it is designing the entire context the model sees. Instructions, retrieved documents, memory, tool outputs — assembling them well is called context engineering.
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The headlines always talk about 'bigger' models. But in real products a quiet trend runs the other way: small, fast, cheap models. Running a giant model for every task is often unnecessary and expensive.
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A single agent can plan and use tools to get work done. But what if a job is run by several agents, each with its own specialty, working like a team? That is the idea of multi-agent systems — a key architectural pattern of 2026.
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Understanding the technology behind ChatGPT, Gemini and Claude from scratch: from next-token prediction to parameters, from context windows to the difference between training and inference, with no technical background required.
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A computer processes numbers, not letters. So how does a word, a sentence, even a meaning turn into a number? From tokenization to embeddings, from vector space to semantic similarity, we explain how AI grasps language with concrete analogies and no math required.
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The same AI model can hand you a forgettable answer from a sloppy prompt and a genuinely useful one from a good prompt. We unpack the few simple but powerful habits behind that gap —clear instructions, context, examples, and step-by-step thinking— with concrete before-and-after comparisons.
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There are two distinct ways to give a language model what it needs to know: leave the book open in front of it (RAG) or send it back to school (fine-tuning). Here is what each one really does, where they diverge, and why the best systems often use both.
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Why does a language model state a wrong answer with total confidence, as if it were true? In plain language, we explain what hallucination is, why it springs from the model's very nature, and how engineers rein it in.
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A single paper published in 2017 changed the course of AI. We walk through why the "Attention Is All You Need" idea matters so much, how words learn to "pay attention" to one another, and why nearly every large language model today rests on this one foundation.
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What separates an "agent" from a plain chatbot? We explain the plan-act-observe loop, tool calling, MCP-style tool standards, and where agents shine and where they fall apart, in plain language.
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From keyword search to semantic search: embeddings, ANN indexes like HNSW, what a vector database actually does, and re-ranking — all explained with plain examples, plus how İçtiHub finds case law by meaning rather than by words.
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Where does bias come from, what do fairness and transparency really mean, and what do KVKK and the EU AI Act actually ask of us? A concrete walkthrough, without the lecture.
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AI is collapsing legal research, drafting, and review from hours into minutes. But it also brings real risks: hallucinated citations, confidentiality leaks, and a quiet creep of over-reliance. This piece explains what genuinely works, what is dangerous, and why the final word still belongs to the lawyer.
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How professionals use AI every day, from writing to research to email to code, how to delegate to it well, and exactly where you should not trust it.
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When an AI can look at a photo and read the text inside it, interpret a chart, or listen to a voice and respond, we call it multimodal AI. We unpack the idea beneath this ability, its real-world uses and its limits, with concrete examples and no technical background required.
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What those leaderboard percentages really mean, where benchmarks mislead, and what actually proves a model works in the real world. A plain-language guide.
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An AI model is either something you rent like a cloud service or something you download like a file and run on your own machine. Choosing between the two is a real trade-off among control, privacy, cost, and capability. Here is what each one means, when each makes more sense, and why mature teams usually run both.
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When you type something into an AI tool, where exactly does your data go? The critical difference between training and inference, the cloud–private–on-prem spectrum, the basics of KVKK and GDPR, the real limits of anonymization, and practical tips that actually work for both individuals and companies.
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A practical roadmap for companies that start with a real problem instead of a slide full of promises — that go small, measure honestly, and know when to walk away.
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What does "AGI" actually mean, where are we really right now, and what does agentic AI change? We calmly separate the optimistic and cautious views, the genuine near-term impact, and the inflated promises.
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We turn hours of manual searching through millions of court decisions and thousands of pages of legislation into semantic research that finishes in seconds — built on large language models and RAG over Turkish legal corpora.
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Why a raw language model gets the law wrong, how Retrieval-Augmented Generation fixes it, and how we build the retrieval layer behind İçtiHub.
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Why Turkish is hard for language models, how that difficulty shapes LLM behavior, and what it really takes to build Turkish-first AI products.
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