11 posts
Metadata design is the foundation of retrieving the right chunk for the right user in RAG: the required field set, authorization scope, date and version fields, and filtered retrieval quality.
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.
RAG didn't die, it matured. Raise retrieval accuracy with late chunking, contextual retrieval, hybrid search, and reranking. A practical 2026 guide for Turkish and KVKK contexts.
What are chunking strategies? Best practices for document splitting in RAG: chunk size, overlap, semantic chunking, and structure-aware methods, end to end.
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 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 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.
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.
One of the most misleading quality failures in enterprise RAG systems is this: the system retrieves the correct file for a query, yet the final answer is still wrong, incomplete, or misleading. At first glance, this may look like a model failure, but the real issue often appears in the finer layers of the retrieval chain. Document-level correctness is not the same as evidence-level correctness. The system may find the right document, yet fail to retrieve the exact section that contains the answer, split meaning through poor chunking, overload the model with noisy context, miss the best passage because reranking is weak, or generate beyond the retrieved evidence. As a result, users face the frustrating question: if the right file was found, why is the answer still incorrect? This guide explains that problem end to end, covering the difference between document-level retrieval and passage-level evidence, chunking strategy, retrieval depth, reranking, context assembly, answer grounding, citation behavior, failure taxonomies, evaluation, and production quality loops.
RAG projects often look impressive in demos but begin to fail in production due to quality, trust, and sustainability problems. In most cases, the root cause is not the model itself, but structural weaknesses in data preparation, retrieval design, evaluation discipline, and prompt behavior. Dirty or outdated documents, weak chunking strategies, poor metadata, missing retrieval evaluation, and underdesigned prompts can push even strong LLMs toward low-trust answers. This guide explains why RAG projects fail and provides a production-oriented framework for building more reliable systems across data preparation, evaluation, and prompt design.
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.