8 posts
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 alignment? Alignment is the effort to make an AI system's goals, behaviors, and outputs consistent with people's true intent and values. This guide: a clear definition, why it matters, how it works (RLHF and Constitutional AI), value alignment, AI safety, reward hacking, Türkiye and enterprise examples, comparisons, 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 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 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.
A head-to-head comparison of the two 2026 flagship AI models — Anthropic Claude Opus 4.7 and OpenAI GPT-5. Architecture and training philosophy differences (Constitutional AI vs RLHF), benchmark results (MMLU, HumanEval, GSM8K, hallucination), Turkish performance, code generation, reasoning, long context (1M vs 256K), multimodal, agent/tool use/MCP, cost, latency, safety, and alignment. Use-case-based winner analysis.
A comprehensive Turkish guide spanning the philosophical foundations of AI ethics and safety to production controls. Covers responsible AI principles (FAT — Fairness, Accountability, Transparency, Privacy, Safety), bias sources and mitigation, hallucination control, alignment techniques (Constitutional AI, RLHF, RLAIF), prompt injection and jailbreak defenses, deepfake detection, red teaming, EU AI Act + ISO 42001 integration, a responsible-AI maturity model, and 3 anonymized Turkish enterprise case studies.
The most current, detailed 2026 Turkish guide to adapting an LLM to your domain. Covers when fine-tuning is necessary, the math behind LoRA, 4-bit training with QLoRA, why DPO beats PPO, modern alternatives (ORPO/KTO/IPO), Turkish dataset sources, GPU/cloud cost modeling, production pipelines, 3 anonymized Turkish enterprise case studies, and KVKK-compliant training. For developers, MLOps engineers, and AI architects.