4 posts
I explain from the field the differences between SFT, DPO and RFT and when to use each: the fine-tuning decision from demonstrations to rewards, with KVKK notes.
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 fine-tuning? Fine-tuning is the method of retraining a pre-trained AI model on a smaller, task-specific dataset to adapt it to a particular behavior, tone, or domain. This guide: a clear definition, how fine-tuning works, types like SFT and LoRA, preparing training data, the difference from RAG, sector examples, data protection, limits, and FAQs.
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.