Showing 193–216 of 540 articles, newest first.
In LLMs the real cost is inference. I explain how I cut cost across the model, system and application layers with caching, routing, quantization and batching.
As agents reach production the real risk is governance. I build the agent inventory, traceability, permission limits and accountability framework from a CTO/CDO lens.
In July 2026 three frontier labs shipped models at once. I compare GPT-5.6, Claude Fable 5, Gemini Deep Think and Grok 4.5 from the field.
With the Digital Omnibus, the EU AI Act's high-risk obligations were delayed to 2027-2028. Is this a cancellation or a reprieve? A practical roadmap for Turkish exporters.
AI in Turkish e-commerce is no longer an experiment. I cover demand forecasting, recommendation engines, agentic customer service and KVKK compliance with field scenarios.
Most agent pilots die before production. The cause is not the model but governance, traceability and context management. I share a solution architecture from the field.
Agentic RAG is not ordinary RAG. I explain the router, ReAct, plan-execute, multi-agent retrieval and self-RAG patterns, and when to choose each.
Do you really need a reranker? When reranking adds value and when it is unnecessary in a RAG retrieval pipeline, cross-encoders, benchmarking, and a decision guide.
What are chunking strategies? Best practices for document splitting in RAG: chunk size, overlap, semantic chunking, and structure-aware methods, end to end.
What is LLM cost optimization? Techniques that cut token cost in production: prompt caching, batching, model routing, prompt trimming, RAG context reduction and FinOps discipline.
What is a Turkish LLM and why is Turkish hard for AI? A comprehensive enterprise guide to morphology, tokenization, model selection, language support and Turkish NLP tasks.
What is LLM evaluation? A comprehensive enterprise guide to eval metrics, benchmark and test set design, LLM-as-judge, calibration, RAG evaluation, and production monitoring.
How is LLM hallucination prevented? A production guide to verification layers: RAG grounding, citations, guardrails, self-verification, output checks, and human oversight.
On-prem LLM deployment guide: hardware requirements, GPU and VRAM, quantization savings, the serving stack, and on-prem vs API total cost of ownership calculation.
Open source LLM comparison: the strengths, licenses, sizes, Turkish performance of Llama, Qwen, Mistral and DeepSeek, plus an enterprise model selection framework.
What is prompt engineering and how is it applied at enterprise scale? Prompt patterns, system prompts, few-shot, chain of thought, prompt management and evaluation guide.
Vector database comparison: we evaluate Qdrant, Milvus, Weaviate, and pgvector for enterprise RAG in terms of scale, performance, cost, data sovereignty, and benchmarking.
pgvector or Qdrant? A 2026 production comparison on latency, hybrid search, and scale, with RAG evaluation metrics and self-hosted options for KVKK.
Even million-token windows lose the middle. Practical context management with the four pillars of context engineering, compression, RAPTOR, and memory systems.
Cutting LLM costs 38-68% with semantic caching, model tiering, and token telemetry. The practical TokenOps playbook and observability stack.
Fine-tuning teaches behavior, RAG brings knowledge. The 'Prompt → RAG → Fine-tune → Distill' decision framework with LoRA/QLoRA adapters, RFT, and small language models.
The 2026 chunking strategy with late chunking, contextual retrieval, and agentic RAG. Which pipeline for which query? A production-oriented decision guide.
Claude Opus 4.8, GPT-5, Gemini 3, Grok 4... In July 2026 there is no 'best model,' only the right one. An enterprise selection framework by task, budget, and KVKK.
GPAI enforcement starts August 2, 2026 with fines up to 3% of turnover or €15M. A 6-week compliance sprint for Turkish companies and the KVKK link.