Showing 1–24 of 510 articles, newest first.
What happens to enterprise AI projects without sponsor support? A field note on budget erosion, slowing decisions, resistance, and how to keep a project alive.
Does microlearning work in enterprise AI training? The retention advantage of short modules, its limit on complex topics, and a practical decision guide for choosing the right format.
What is model serving? A guide to the serving layer: from managed APIs to self-hosted inference servers like vLLM, TGI and Ollama; batching, streaming, concurrency, throughput and latency trade-offs.
How to build an AI roadmap? A 12-month phased plan: foundation, first use case, expansion and scale phases, gate criteria for each phase, and realistic milestones.
How is Türkiye's AI ecosystem? A guide to the actors — startups, enterprises, academia-industry, public sector, investors — their strengths, gaps, and the talent pool.
AI ethics is not a poster of principles; this guide turns each principle into a control point, a testable criterion, an assigned owner, and an auditable trail across the delivery lifecycle.
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.
Metadata design is the foundation of retrieving the right chunk for the right user in RAG: the required field set, authorization scope, date and version fields, and filtered retrieval quality.
When is a multi-agent system really necessary? The single-agent vs multi-agent decision, agent division of labor, coordination cost, error propagation, and orchestration patterns for enterprise AI.
How is the software developer's role changing in the age of AI? Code-assistant impact, rising review load, the shifting role, and the new skills to learn — in this guide.
An integration-delay experience from the field. Why do legacy system APIs, data access permissions, missing test environments and data-mapping exceptions push project timelines?
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.
The false-alarm economy in computer vision models: the differing cost of false positives and negatives, threshold tuning, the sensitivity-specificity trade-off, and managing operator load in production.
LLM monitoring is the discipline that makes a production language-model system's quality, cost, and performance visible through logging, tracing, and alerts. What to log and how to measure it.
Small language model or large model? An enterprise decision framework in light of task-based selection, the cost-performance balance, and the hybrid architecture trend.
How is training impact measured? Four measurement levels from satisfaction to behavior change, tracking learning transfer, and an evidence-collection playbook.
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 automated decision-making, which decisions fall under KVKK, and how are the right to object, human intervention, and transparency ensured? A practical design guide.
RAG evaluation is a methodology that measures retrieval quality (recall@k, MRR, nDCG) and generation quality (faithfulness, answer relevance) separately. A layer-by-layer guide.
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.
A lack of data governance quietly drives AI projects into a dead end: ownership gaps, data quality problems, source ambiguity. A field observation with symptoms and minimum safeguards.
The art of giving context: how much information should you give an AI model? Too little context yields incomplete answers, too much creates noise. A guide to relevant selection, ordering, and measurement.
The AI agenda floods in with dozens of announcements a day. This guide teaches you to separate signal from noise, filter out hype, and evaluate sources — step by step.
How is the AI ecosystem structured? We explain the hardware, model, orchestration, application, and consulting layers — and who sits where — with a clear layer map for enterprise buyers.