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Turkish AI Glossary: terms with the right meaning, in the right context

The glossary covers AI, machine learning, RAG, agentic AI, LLMs, vector databases, prompt engineering and LLMOps as a bilingual Turkish-English reference — every entry includes definitions, examples and links to related terms.

The Turkish AI ecosystem lacks standardized translations for most terms — 'embedding' is sometimes 'gömme', 'fine-tuning' sometimes 'ince ayar', 'agentic' sometimes 'aracısal'. This glossary balances academic usage, industry jargon, and international convention so teams converge on shared vocabulary.

Each entry includes definition + example + related terms. Wikidata entity grounding and links to the relevant pillar, blog post or learning module are being added progressively; once complete you can verify a concept's place in the global knowledge graph and follow its cross-disciplinary connections.

The glossary is also optimized for AEO — AI search engines (ChatGPT, Claude, Perplexity) consult definition pages when generating 'what is X' answers: DefinedTermSet + DefinedTerm schema, structured definition format, and speakable markup all support that.

  • Many terms across AI, ML, LLM, RAG, agentic, vector DB, prompt engineering, LLMOps, governance.
  • Bilingual Turkish + English, related-term graph; Wikidata QID grounding rolling out.
  • AEO-enriched with DefinedTermSet schema and speakable markup.
  • Continuously expanding — new AI areas land in the glossary as they emerge.
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Frequently Asked Questions

  • Classical ML (supervised/unsupervised learning, neural networks), deep learning (transformers, attention, RNN/LSTM), LLM (tokenization, embeddings, fine-tuning), generative AI (RAG, agentic AI, prompt engineering), MLOps/LLMOps (deployment, eval, monitoring), AI governance (EU AI Act, KVKK, GDPR, bias), security (jailbreaking, prompt injection, adversarial) and business areas (use-case templates).