Showing 289–312 of 510 articles, newest first.
What is explainable AI? Explainable AI (XAI) is the set of methods that make a model's decision — which inputs and reasons it rests on — understandable to a human. This guide: a clear definition, the black-box problem, how XAI works, SHAP and LIME, model transparency, KVKK/GDPR, sector examples, and FAQs.
What is anomaly detection? Anomaly detection is the process of automatically finding observations (outliers) in a dataset that deviate markedly from the expected pattern. This guide: a clear definition, how it works, methods like isolation forest, fraud detection and time series anomaly examples, supervised/unsupervised approaches, common mistakes, and FAQs.
What are AI Overviews? AI Overviews is an AI-powered answer box at the very top of Google's search results, produced by a language model that summarizes multiple sources. This guide: a clear definition, how Google's generative results work, source selection logic, zero-click and click-through-rate impact, SEO/GEO strategy, and FAQs.
What is AI hallucination? AI Hallucination is when a language model produces information that does not actually exist but sounds correct and confident. This guide: a clear definition, why it happens, hallucination types, real-world examples, preventing hallucination with RAG and grounding, fact-checking, comparisons, and FAQs.
What is a vector database? A vector database is a specialized database that stores numerical vectors (embeddings) representing the meaning of text, images, or audio, and quickly finds the records closest in meaning to a query. This guide: a clear definition, how it works, similarity search and the HNSW index, tools like Qdrant, its relationship to RAG, the difference from classic databases, KVKK, and FAQs.
What is RLHF? RLHF (Reinforcement Learning from Human Feedback) is an alignment method that scores a language model's outputs by human preference and, using a reward model that learns those preferences, retrains the model. This guide: a clear definition, how RLHF works, SFT vs the reward model, a DPO comparison, real-world examples, limits, and FAQs.
What is a reranker? A reranker is a model that re-orders the candidate documents retrieved in the first stage of a search or RAG system according to their true relevance to the query. This guide: a clear definition, why it is needed, how it works, the cross-encoder architecture, two-stage retrieval, its place in the RAG pipeline, retrieval quality, and FAQs.
What is KVKK-compliant AI? KVKK-compliant AI means building an AI system that processes personal data so that it conforms — from the design stage — to the legal basis, disclosure, data minimization, and security principles of Turkey's Law No. 6698 (KVKK). This guide: a clear definition, why it matters, how it works, on-premise AI, data anonymization, explicit consent, a compliance checklist, and FAQs.
What is a guardrail? A guardrail is a safety layer that inspects a large language model's inputs and outputs against predefined rules and blocks harmful or out-of-policy behavior. This guide: a clear definition, how it works, input filtering and output validation, content moderation, LLM safety, GDPR, comparisons, and FAQs.
What is GraphRAG? A RAG architecture that feeds a language model over a knowledge graph instead of flat text chunks. The difference from vector RAG, the hybrid architecture decision, entity and relationship extraction, graph-based retrieval, cost, and a decision framework.
What is the EU AI Act? The EU AI Act (European Union Artificial Intelligence Act) is the world's first comprehensive horizontal AI regulation, classifying AI systems by the risk they pose and imposing obligations accordingly. This guide: a clear definition, risk classification, high-risk systems, the compliance timeline, the effect on Türkiye, GPAI rules, penalties, and FAQs.
What is a context window? A context window is the maximum length of text, measured in tokens, that a language model can process at once and take into account while generating a response. This guide: a clear definition, how it works, token limit, long context, memory management, the need for RAG, model comparison, and FAQs.
What is chunking? Chunking (document splitting) is the process of dividing a long text into processable, meaningful pieces (chunks) for RAG and search systems. This guide: a clear definition, why chunking is the foundation of RAG performance, how it works, choosing chunk size and chunk overlap, fixed/recursive/semantic chunking types, examples, KVKK, common mistakes, and FAQs.
What is an open-source LLM? An open-source LLM is a large language model whose weights are published openly so you can run it on your own infrastructure. This guide: a clear definition, open weights vs open source, local deployment, licensing, Llama and other models, KVKK/GDPR, comparison with closed models, and FAQs.
What is AI governance? AI Governance is the framework of policies, roles, and controls through which an organization develops, deploys, and audits its AI systems in line with law, ethics, and business goals.
What is data anonymization? Data anonymization is the irreversible transformation of personal data in a dataset so it can no longer be linked to an individual. This guide: a clear definition, data masking, the difference from pseudonymization, k-anonymity, the KVKK dimension, AI training, common mistakes, and FAQs.
What is logistic regression? Logistic regression is a statistical machine learning model used for binary classification that estimates the probability of an observation belonging to a given class. This guide: a clear definition, the sigmoid function, odds ratio, difference from linear regression, how it works, industry examples, multiclass variants, common mistakes, and FAQs.
What is embedding? An embedding turns a text into a vector representing its meaning. Semantic search, Turkish embedding models, vectors, similarity and vector databases in this guide.
What is AI consulting? AI consulting is an expert service that determines which problems an organization should use AI for, how, and with what risks, then turns this into an actionable roadmap. This guide: a clear definition, the consulting process, AI strategy, choosing a consultant, pricing, KVKK/GDPR, and FAQs.
What is reinforcement learning? Reinforcement learning is a machine learning approach where an agent learns the best behavior through trial and error inside an environment, based on the rewards and penalties it receives. This guide: a clear definition, agent and environment, the reward function, how it works, Q-learning, deep RL, RLHF, real-world examples, the difference from supervised learning, limits, and FAQs.
What is AI literacy? AI literacy is the competence to understand how AI systems work and to use these tools critically, ethically, and effectively. This guide: a clear definition, why it matters, its components, the difference from digital literacy, in-house training, the AI champion model, GDPR, and FAQs.
What is enterprise AI training? Enterprise AI training is a structured, organization-specific learning program that teaches a company's employees to use AI tools safely and productively according to their roles. This guide: a clear definition, why it matters, how it is designed, program selection, curriculum, the AI trainer, impact measurement, KVKK/GDPR, and FAQs.
What is a neural network? A neural network is a machine learning model inspired by the neurons in the human brain, learning patterns from data through connected layers of artificial neurons. This guide: a clear definition, neuron and layer structure, activation functions, learning via backpropagation, types of neural networks, real-world examples, its relation to deep learning, and FAQs.
What is data mining? Data mining is the discovery of previously unknown, useful patterns and relationships in large volumes of data using statistics and machine learning. This guide: a clear definition, pattern discovery, clustering and classification, association rules, the CRISP-DM process, real-world examples, KVKK/GDPR, and FAQs.