AI, explained without the hype
We answer the questions people actually ask when building enterprise AI: what RAG is, when fine-tuning is the right call, whether the model should run on your own servers, and where KVKK fits in. Not a brochure — what we learned building these systems.
What Is RAG, and How to Build It Inside Your Organization
RAG connects a language model to your own documents. The definition, the five-link architecture, a step-by-step build, a config example, and — just as important — when it is the wrong tool.
ReadRAG or fine-tuning: which one, and when
RAG gives a model information; fine-tuning teaches it behaviour. Treating them as alternatives is one of the costliest mistakes in enterprise AI. A clear comparison across cost, freshness, data privacy, setup time and hallucination control — plus the right order for a hybrid build, and the misconceptions we meet most often.
ReadSemantic search vs. keyword search: what actually changes
Semantic search matches meaning; BM25 matches words. The real difference, how hybrid retrieval with RRF works, how to set it up in Elasticsearch, and where Turkish morphology actually fits in — without the hype.
ReadSelf-hosted LLM or cloud API
Choosing between an on-premise LLM and a cloud API is a risk and cost decision, not a technology preference. A side-by-side comparison across data sovereignty, cost model, latency, maintenance and Turkish cross-border transfer rules — plus realistic hardware expectations and decision criteria.
ReadHow to build a KVKK-compliant AI system in Türkiye
Putting personal data and an LLM in the same sentence is an architectural decision in Türkiye. Cross-border transfer, the limits of explicit consent, data minimisation, disclosure duties and log retention — a compliance-aware framework for engineering teams.
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