28 posts
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.
What is few-shot prompting? It is the technique of guiding a model by showing it a few examples of the task. How many examples, how to pick them, and the difference from zero-shot, in this guide.
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.
“Fine-tuning or RAG?” is a false dilemma. The 2026 sequence: Prompt → RAG → Fine-tune → Distill. LoRA/QLoRA, small language models, distillation, and the KVKK-sensitive self-host decision.
RAG for changing knowledge, fine-tuning for stable behavior; often both together. Why LoRA is the default, when RFT, small models, and the KVKK advantage.
Synthetic data solved fine-tuning's data bottleneck. A field guide to generation methods, the practical recipe, model collapse, and Turkish data scarcity.
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.
Choose SFT, DPO/ORPO/KTO or RFT/GRPO by the data you have. A 2026 fine-tuning guide: method table, RAG comparison, KVKK, and a project checklist.
RAG or fine-tuning? Wrong question. The right sequence: Prompt → RAG → Fine-tune → Distill. A 2026 decision framework covering LoRA/QLoRA, GRPO, small language models, and KVKK.
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.
Open source LLM comparison: the strengths, licenses, sizes, Turkish performance of Llama, Qwen, Mistral and DeepSeek, plus an enterprise model selection framework.
Fine-tuning teaches behavior, RAG brings knowledge. The 'Prompt → RAG → Fine-tune → Distill' decision framework with LoRA/QLoRA adapters, RFT, and small language models.
RAG or fine-tuning? A decision framework, cost comparison, decision matrix, and hybrid approach: which scenario calls for RAG and which for fine-tuning, in this comprehensive guide.
Fine-tuning shapes behavior; RAG supplies knowledge. The right 2026 order: prompt first, then RAG, then LoRA/QLoRA, distillation last. A field decision guide.
RFT, LoRA and QLoRA — when to fine-tune vs use RAG. The form-vs-facts rule and KVKK-compliant training data.
What is LoRA? LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning method that adapts a large language model by adding small, trainable matrices next to its frozen weights instead of changing all of them. This guide: a clear definition, how LoRA works, QLoRA and variants, the adapter idea, fine-tuning cost, comparison with full fine-tuning, 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.
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 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.
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 2026 fine-tuning stack: base → SFT → DPO. I explain preference optimization, LoRA/QLoRA, and when to fine-tune instead of using RAG, from the field.
Small language models and fine-tuning: cost-effective customization with LoRA, QLoRA, and distillation. When an SLM beats a big API, and RAG vs FT.
A concrete roadmap to land a global remote AI Engineer position from zero in 12 months: 5 production-level projects, GitHub portfolio + blog strategy, $200K+ offer. Karpathy, Raschka, 3Blue1Brown, Andrew Ng curriculum; HuggingFace + LangChain + Anthropic Academy free programs; Turkish alternatives; case study (14-month timeline); and interview strategy for top offers.
A 2026 snapshot of the Turkish open-source LLM ecosystem: Trendyol-LLM, Cosmos-Llama, KanarYa, Kumru AI, the TÜBİTAK BİLGEM domestic model, and the T3 AI Baykar defense model. Detailed decision guide covering MMLU-TR and TUMLU benchmarks, licensing, tokenization gap, VRAM requirements, self-hosting needs, and which model to pick for which use case.
Detailed comparison of the three most powerful 2026 open-weight LLM families — DeepSeek (V3 + R1), Qwen (2.5 + 3), and Meta Llama (4). Architecture (MoE vs dense), benchmarks (MMLU, HumanEval, GSM8K), Turkish performance, license (MIT vs Apache vs Llama Community), cost (self-hosted vs API), hardware (VRAM, GPU), fine-tune friendliness, ecosystem (Hugging Face, vLLM, Ollama), KVKK / data sovereignty advantages. Use cases for Turkish enterprises.
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.
Three of the most commonly confused concepts in deep learning are transfer learning, fine-tuning, and representation learning. They are not the same thing, but they are tightly connected. Representation learning refers to learning useful and generalizable internal features from data. Transfer learning is the broader strategy of reusing knowledge learned in one task or domain for another task or domain. Fine-tuning is often the practical adaptation mechanism used to realize that transfer. Put differently, strong representations make transfer possible, transfer learning defines the reuse logic, and fine-tuning operationalizes it. This guide explains the historical development, conceptual relationship, practical differences, and enterprise relevance of these three ideas in modern AI systems.
One of the most important strategic questions in enterprise AI is whether a problem should be solved with RAG or with fine-tuning. Many teams treat these approaches as direct alternatives, but in reality they solve different classes of problems. RAG strengthens access to current and source-grounded knowledge, while fine-tuning shapes model behavior and task adaptation. This guide compares RAG and fine-tuning across technical, operational, cost, governance, and production-readiness dimensions, and explains when each approach is the right choice—and when a hybrid architecture makes more sense.