Showing 337–360 of 510 articles, newest first.
What is RAG? RAG (Retrieval-Augmented Generation) is an architecture that feeds a language model with external knowledge before it answers. Embeddings, vector databases, chunking, reranking and hallucination reduction in this guide.
What is Agentic AI? A clear definition of agentic (agent-based) AI, its difference from an AI agent, the perceive-plan-use-tools-evaluate loop, multi-agent orchestration, MCP, workflow automation, KVKK, and enterprise use in this comprehensive guide.
What is a prompt? A prompt is the input that tells an AI model what you want it to do — a question, instruction, or context. This guide: a clear definition, the components of a good prompt, techniques for writing effective prompts, examples, the prompt–output quality relationship, and FAQs.
What is MCP? The Model Context Protocol is an open standard connecting AI models to tools and data — the USB-C of AI. Architecture, MCP servers, agents, and enterprise integration in this guide.
What is prompt engineering? Prompt engineering is the systematic design of inputs to reliably get the desired output from a language model. This guide: a clear definition, core techniques, few-shot and chain of thought, the system prompt, enterprise use, limits, and FAQs.
What is a token? A token is the smallest unit of meaning a language model uses to process text — it can be a word, a word piece, or punctuation. This guide: a clear definition, how tokenization works, the token–context window relationship, LLM cost, and why API pricing is token-based.
What is an LLM? How do Large Language Models (LLMs) work, what does Transformer architecture solve, what are tokens, embeddings, and context windows, and how do GPT-5, Claude Opus 4.7, Gemini 3, and Llama 4 compare? A comprehensive 2026 reference covering Turkish LLM performance, training stages, hallucination control, and cost modeling.
What is artificial intelligence? Artificial intelligence (AI) is the set of disciplines that enable machines to imitate human-like learning, reasoning, perception, and decision-making. This guide is a 2026 reference covering AI's definition, types, core technologies, industry applications, and Turkey-specific regulatory context.
Underwriting from 3 days to 3 minutes, straight-through processing to 70-90%, fraud detection improving 30%+. A practical AI roadmap for Turkish insurers with EU AI Act/KVKK reality.
98% of organizations use unsanctioned AI. Bans don't solve it; enablement does. A guide to bringing shadow AI into the light, the build/buy/assemble decision, and aligning with KVKK/EU AI Act.
RFT rewards reasoning on verifiable tasks. I explain GRPO, DPO and PPO, when to combine with RAG, and the LoRA ladder from the field.
The era of tuning prompts by hand is ending. Automatic optimization with DSPy, MIPROv2 (10-40% lift) and GEPA (beats MIPROv2 by 13%, 35x efficient). A practical guide in a Turkish/KVKK context.
The OpenTelemetry GenAI Semantic Conventions standardized LLM tracing. A guide to building an observability pipeline for production LLM systems with token, cost, quality and KVKK balance.
LLM-as-a-judge is the dominant automated evaluation method but biased; RAGAS human correlation is only 0.55. A reliable eval guide covering best practices, biases, in a Turkish/KVKK context.
On multi-hop questions GraphRAG scores 86%, vector RAG 32%. But the right answer for most is a hybrid router. An architecture decision framework in a Turkish/KVKK context.
The GPT-5.6, Claude Sonnet 5, Gemini 3.2 and Qwen/DeepSeek wave. 'Best model' is the wrong question; which model for which job is the right one. A decision framework with Turkish, cost and KVKK reality.
How the Digital Omnibus changed the EU AI Act, what the 2 August 2026 GPAI enforcement brings, and what Turkish companies touching the EU market should do in 60 days.
What makes agents powerful is not the model but memory. Four memory types, the orchestrator-worker architecture, computer-use agents, and human-in-the-loop design under KVKK.
Getting reliable JSON from an LLM in production takes more than a prompt. How to guarantee schema conformance with JSON mode, structured outputs, constrained decoding, and function calling.
Which vector database is right for RAG and agents? I compare pgvector, Qdrant, Milvus, and Weaviate on performance, scale, hybrid search, and data sovereignty.
If your RAG talks nonsense, the problem is usually retrieval, not generation. How to lift quality with BM25 + dense hybrid search, RRF, and cross-encoder reranking.
Prompt caching cuts input token cost up to 90% on Anthropic and 50% on OpenAI. I cover both approaches, when to pick which, and cache-hit design patterns.
OpenTelemetry GenAI conventions make LLM and agent systems observable in production. Track tokens, cost, latency, and quality while escaping vendor lock-in.
Over 80% of employees use unapproved AI tools, yet only 37% of firms have a policy. How to govern shadow AI without killing innovation.