60 posts
What is chain of thought? Chain of thought is a reasoning method where a language model answers a question by thinking step by step. When it helps, when it doesn't, and how reasoning models changed it.
What is quantization? Quantization is a model-compression technique that represents a model with fewer bits to save memory and gain speed, at the price of a measurable quality loss. INT8, INT4, PTQ, QAT and more.
What is a GPU? A GPU is a processor that performs parallel computation with thousands of cores and forms the heart of AI hardware. VRAM, CPU vs GPU, training vs inference in this guide.
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.
How to build on-premise AI infrastructure? Hardware, model serving, scaling, monitoring, updates and the real operational burden — an enterprise decision guide.
What is the difference between an AI agent and a chatbot? A chatbot talks and answers; an AI agent plans toward a goal, uses tools, and completes multi-step tasks autonomously.
How to size hardware for an on-premise LLM: a practical guide to VRAM math, quantization, concurrent users, GPU count and server sizing for enterprise deployments.
Writing a system prompt means building the role, constraint, and output-format layers correctly. We break the prompt structure down layer by layer.
How to read LLM benchmark scores? A practical guide to reading model comparisons with an eye on data contamination, real performance, and the evaluation limit.
How do you do tool definition in agent architecture? A good tool schema: a clear name, a description that says when to use it, well-typed parameters, and readable errors.
A guide to setting up an eval set: designing the golden question set, scoring rubric, human evaluator agreement, acceptance threshold and regression testing for LLM evaluation.
How is enterprise RAG built? Pipeline layers, document preparation, retrieval, generation and quality measurement; a technical guide to enterprise RAG architecture and setup.
Where does an open source LLM stand in enterprise use? License terms, closed-model comparison, operational load, and in which scenario it makes sense.
What is prompt engineering? The deliberate design of the instruction that gets the output you want from a language model: role, task, context, constraint, and output format.
Prompt engineering is now engineering, not art. Automated optimization with DSPy, an eval-driven workflow, structured output, prompt chaining, and Turkish-specific evaluation.
The August 2026 frontier landscape: neck-and-neck on SWE-bench Verified, separation on SWE-bench Pro. Which model for which job, benchmark literacy, and the Turkish-performance criterion.
In July 2026 three major providers released new frontier models. An enterprise-use comparison, multi-model strategy, and a right-choice guide.
RAG or long context in the million-token era? A practical guide to hybrid architecture, BM25 + vector, RRF, reranking, contextual retrieval, and the KVKK angle.
Measure your RAG system's real quality with four core metrics: faithfulness, answer relevance, context precision, and recall. Ragas, LLM-as-a-judge, and Turkish challenges.
As Gartner puts it, context engineering is in, prompt out. How the role evolved, patterns that still work (CoT, few-shot), and managing context at enterprise scale.
Observability is now a production prerequisite. Tracing vs evaluation, hallucination detection, LangSmith/Langfuse/MLflow, and self-hosting options for KVKK.
The July 2026 LLM API price table, a TCO framework, and cost-cutting levers. Why cheapest isn't always right, plus the KVKK/data-residency dimension.
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 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 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.
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.
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.
How is a RAG architecture built? An end-to-end pipeline, chunking, embedding, vector database, hybrid search, reranking, generation, and evaluation in a step-by-step production guide.
Frontier models as of July 2026: benchmarks, price/performance and an enterprise selection guide. Which model for which job? Practical field notes.
Claude Sonnet 5, Gemini 3.5 Flash, GPT-5.6 and open-weight models. A use-case model-selection framework with a cost/latency table.
What is an AI engineer? An AI Engineer builds and integrates AI features into real applications using ready-made foundation models and APIs. This guide: a clear definition, the ML engineer difference, required skills, career path, salary range, the Türkiye ecosystem, limits, and FAQs.
What is a system prompt? A system prompt is the high-level instruction given to a language model before a conversation starts, defining its role, behavior rules, and boundaries, which the user does not see. This guide: a clear definition, how a system prompt works, the difference from a user prompt, prompt hierarchy, types, real-world and Türkiye examples, security, and FAQs.
What is prompt injection? Prompt injection is an AI security vulnerability where hidden instructions are inserted into a language model's input to make the model break the developer's rules. This guide: a clear definition, how prompt injection works, direct and indirect injection, its difference from jailbreak, real-world examples, LLM security, defense methods, and FAQs.
What is LLM observability? LLM observability is the practice of tracing every request of a language model application end to end, making prompts, responses, latency, cost, and quality visible. This guide: a clear definition, why it matters, how tracing works, Langfuse and OpenTelemetry, production monitoring metrics, evaluation, KVKK, and FAQs.
What is Llama? Llama is a family of large language models (LLMs) developed by Meta whose model weights are released openly to everyone. This guide: a clear definition, how Llama works, what an open-weight model means, versions and variants, running it as a local LLM, the Llama license, enterprise and Türkiye use cases, KVKK, comparison with closed models, and FAQs.
What is function calling? Function calling is when a language model understands a natural-language request and produces, as structured JSON, which predefined function to call and with what arguments. This guide: a clear definition, why it is needed, how it works, JSON schema, tool use, API integration, its relation to MCP, security, and FAQs.
What is DeepSeek? DeepSeek is a family of open-source LLMs and reasoning models developed by a Chinese research company, notable for its low-cost training and MoE architecture. This guide: a clear definition, how DeepSeek works, MoE architecture, reasoning-model logic, what being an open-source LLM means, enterprise use, GDPR, comparison with other models, and FAQs.
What is AI hallucination? AI Hallucination is when a language model produces information that does not actually exist but sounds correct and confident. This guide: a clear definition, why it happens, hallucination types, real-world examples, preventing hallucination with RAG and grounding, fact-checking, 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 a guardrail? A guardrail is a safety layer that inspects a large language model's inputs and outputs against predefined rules and blocks harmful or out-of-policy behavior. This guide: a clear definition, how it works, input filtering and output validation, content moderation, LLM safety, GDPR, comparisons, and FAQs.
What is a context window? A context window is the maximum length of text, measured in tokens, that a language model can process at once and take into account while generating a response. This guide: a clear definition, how it works, token limit, long context, memory management, the need for RAG, model comparison, and FAQs.
What is an open-source LLM? An open-source LLM is a large language model whose weights are published openly so you can run it on your own infrastructure. This guide: a clear definition, open weights vs open source, local deployment, licensing, Llama and other models, KVKK/GDPR, comparison with closed models, and FAQs.
What is embedding? An embedding turns a text into a vector representing its meaning. Semantic search, Turkish embedding models, vectors, similarity and vector databases in this guide.
What is GPT? GPT (Generative Pre-trained Transformer) is a family of transformer-based language models pre-trained on large text data that generate text by predicting the next word. This guide: a clear definition, how GPT works, the transformer and pre-training, the autoregressive model logic, GPT vs ChatGPT, OpenAI and versions, enterprise use, 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.
What is LLMOps? The discipline of running large language models in production: MLOps difference, prompt management, evaluation, LLM observability, cost optimization, guardrails, and a maturity model.
What is ChatGPT? ChatGPT is a generative AI chat assistant developed by OpenAI that produces text, answers questions, and completes tasks by conversing in natural language. This guide: a clear definition, how it works, its relationship to the GPT model, how to use ChatGPT, free vs paid, its limits, KVKK, and FAQs.
What is generative AI? Generative AI is a type of AI that produces new content — text, images, audio, video, and code — using patterns learned from data. This guide: a clear definition, how it works, the role of large language models and diffusion models, types of content generation, enterprise use, and limits.
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 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.
A comprehensive reference for designing, scaling, and shipping Retrieval-Augmented Generation (RAG) systems in production with KVKK compliance. Covers Turkish-capable embedding model selection, vector DB comparison, chunking, hybrid search, re-ranking, hallucination control, eval harness, and three anonymized Turkish enterprise case studies — end-to-end production architecture.