Understanding a term from zero, with examples — from definition to application.
What is chain of thought? Chain of thought is a reasoning method where a language model answers a question by thinking step by step. When it helps, when it doesn't, and how reasoning models changed it.
What is a use case? A definition of a business problem in which an actor, given a trigger and defined input, reaches a measurable output — with a success metric and clear scope.
What is quantization? Quantization is a model-compression technique that represents a model with fewer bits to save memory and gain speed, at the price of a measurable quality loss. INT8, INT4, PTQ, QAT and more.
What is AI literacy? It is the baseline understanding of what AI can and cannot do, its limits, and how to verify its output; not technical expertise but a skill for everyone.
What is LLM latency? Time to first token (TTFT), per-token time, streaming, and user perception: the components that determine response time and acceptable thresholds.
What is digital maturity? It is a capability level, measured across dimensions, showing how well an organization turns technology, data, talent, and culture into business value.
What is a GPU? A GPU is a processor that performs parallel computation with thousands of cores and forms the heart of AI hardware. VRAM, CPU vs GPU, training vs inference in this guide.
What is a multimodal model? An AI model that processes image, text, and audio together in a single model. Vision models, document understanding, the OCR difference, and use cases.
What is few-shot prompting? It is the technique of guiding a model by showing it a few examples of the task. How many examples, how to pick them, and the difference from zero-shot, in this guide.
What is sovereign AI? Holding control over data, model, and infrastructure without outside dependency: data sovereignty, model independence, and local infrastructure.
What is a high-risk AI system under the EU AI Act? Risk classes, the Annex III list, conformity assessment, and the obligations that follow, explained with a table and FAQ.
What is prompt engineering? The deliberate design of the instruction that gets the output you want from a language model: role, task, context, constraint, and output format.
What is data quality? Data quality is the sum of dimensions — accuracy, completeness, consistency, timeliness, uniqueness, and validity — that determine data's fitness for its intended use. This guide: a clear definition, the six quality dimensions, measurement metrics, the data cleaning process, the impact on AI and RAG projects, common mistakes, and FAQs.
What is data governance? Data governance is the set of policies and processes that define the ownership, quality, security, and usage rules of data in an organization. This guide: a clear definition, the difference from data management, core components, the KVKK dimension, its role in AI projects, implementation steps, and FAQs.
DPO aligns LLMs to human preferences without a separate reward model or RL. Loss intuition, beta, preference data, RLHF comparison, KVKK, and a Turkish example.
What is ISO 42001? The AI management system (AIMS) standard, Annex A controls, the certification process, EU AI Act alignment, and the situation in Türkiye.
What is an AI roadmap? An AI roadmap is a strategic document where an organization, starting from its business goals, prioritizes AI use cases, measures its current state with a maturity model, and usually crystallizes it as a 12-month plan. This guide: a clear definition, why it is needed, how to build it, the maturity model, use-case prioritization, its relation to AI strategy, common mistakes, and FAQs.
What is an AI engineer? An AI Engineer builds and integrates AI features into real applications using ready-made foundation models and APIs. This guide: a clear definition, the ML engineer difference, required skills, career path, salary range, the Türkiye ecosystem, limits, and FAQs.
What is cross entropy? Cross entropy is a loss function that measures the difference between the probability distribution a model predicts and the true label. This guide: a clear definition, its relation to entropy, its link with softmax, binary and multi-class cross entropy, why it is the standard classification loss, real-world examples, and FAQs.
What is SMOTE? SMOTE (Synthetic Minority Over-sampling Technique) is an over-sampling method that fixes class imbalance by generating new, synthetic examples of the minority class in imbalanced datasets. This guide: a clear definition, why SMOTE is needed, how it works, its variants, real-world and Türkiye examples, its limits, and FAQs.
What is likelihood? Likelihood is a function that, holding the observed data fixed and varying a model's parameters, measures how well those parameters explain the data. This guide: a clear definition, the likelihood-vs-probability difference, maximum likelihood, log-likelihood, parameter estimation, the link to AI, and FAQs.
What is a multi-agent system? A multi-agent system is an architecture where several AI agents, each with its own role, solve a task together by dividing the work and communicating with one another. This guide: a clear definition, the difference from a single agent, how it works, agent orchestration, task division, LangGraph and CrewAI, real-world examples, limits, and FAQs.
What is facial recognition? Facial recognition is a biometric verification technology that turns a human face in an image into a numerical signature and compares it with enrolled faces to establish identity. This guide: a clear definition, how it works, the difference from face detection, use types, security applications, the KVKK dimension, limits, and FAQs.
What is YOLO? YOLO (You Only Look Once) is a real-time object detection architecture that locates and classifies objects in an image in a single neural network pass. This guide: a clear definition, why YOLO matters, how it works, bounding boxes and the grid, YOLO versions, sector examples from Türkiye, KVKK, comparisons, and FAQs.
Grouped by format, newest first within each group.
Situations genuinely encountered in consulting and training engagements — where theory cracks in practice.
An answer to “which one should I pick?” with a criteria table and an explicit decision rule.
A step-by-step, executable path — with prerequisites, outputs and typical pitfalls.
The data behind a development and what it means operationally — consequences, not headlines.
Beyond the overview: architectural decisions, production patterns and measured results.
Every option in one area, in one place, screened against the same criteria.
A subject treated in depth, without a fixed template.