21 posts
The embedding model choice determines the fate of Turkish RAG quality. Multilingual vs Turkish-specific, dimension and performance, reading benchmarks, and building your own evaluation.
How is enterprise RAG built? Pipeline layers, document preparation, retrieval, generation and quality measurement; a technical guide to enterprise RAG architecture and setup.
In RAG, chunking strategy is not one setting; it varies by document type. The right chunk size, overlap ratio and method for contracts, tables and manuals.
In 2026 'vector as a feature' wins: PostgreSQL + pgvector suffices for most scenarios. Three architectural thresholds, evaluation traps, and selection criteria.
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.
Vector database comparison: we evaluate Qdrant, Milvus, Weaviate, and pgvector for enterprise RAG in terms of scale, performance, cost, data sovereignty, and benchmarking.
The 2026 chunking strategy with late chunking, contextual retrieval, and agentic RAG. Which pipeline for which query? A production-oriented decision 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.
What is semantic search? Semantic search is a retrieval method that, instead of matching a query word by word, compares the meaning of the query and documents using embedding vectors to return the most relevant results. This guide: a clear definition, the difference from keyword search, how it works, semantic similarity, hybrid search, examples, and FAQs.
What is a vector database? A vector database is a specialized database that stores numerical vectors (embeddings) representing the meaning of text, images, or audio, and quickly finds the records closest in meaning to a query. This guide: a clear definition, how it works, similarity search and the HNSW index, tools like Qdrant, its relationship to RAG, the difference from classic databases, KVKK, and FAQs.
What is chunking? Chunking (document splitting) is the process of dividing a long text into processable, meaningful pieces (chunks) for RAG and search systems. This guide: a clear definition, why chunking is the foundation of RAG performance, how it works, choosing chunk size and chunk overlap, fixed/recursive/semantic chunking types, examples, KVKK, common mistakes, 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 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.
If your RAG talks nonsense, the problem is usually retrieval, not generation. How to lift quality with BM25 + dense hybrid search, RRF, and cross-encoder reranking.
Your embedding model decides the fate of Turkish RAG. Comparing Qwen3, Cohere embed-v4, OpenAI, and BGE-M3 — plus how to evaluate on your own Turkish data.
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.
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.
Large language models have become one of the most influential technologies in modern AI. Yet they are often explained too superficially, as if they were merely “text prediction engines trained on huge amounts of data.” While that description is not entirely wrong, it is far from sufficient. Without understanding transformer architecture, tokenization, self-attention, representation learning, and inference dynamics, it is impossible to understand how LLMs actually behave. This guide provides a systematic and technically grounded explanation of how large language models work, from tokens and embeddings to transformer blocks, attention, training, inference, and sampling.
Enterprise RAG systems are one of the most powerful ways to connect large language models with internal company knowledge in a reliable, auditable, and source-grounded way. But building a production-grade RAG architecture is far more than uploading documents into a vector database. Source selection, parsing, chunking strategy, embeddings, metadata design, hybrid retrieval, reranking, prompt assembly, evaluation, observability, security, and governance all need to work together. This guide explains how to design an enterprise RAG system end to end and what it really takes to make chunking, retrieval, and reranking decisions that improve quality in production.