24 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.
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.
Pure vector search misses exact terms; pure keyword search misses meaning. A practical guide to combining BM25 and vector search in RAG with RRF and contextual retrieval.
Stateless LLM calls aren't enough for agents. Short/long-term memory, episodic-semantic-procedural memory, and practical architecture in light of KVKK's Agentic AI guideline.
In 2026 'vector as a feature' wins: PostgreSQL + pgvector suffices for most scenarios. Three architectural thresholds, evaluation traps, and selection criteria.
2026 vector database guide: Pinecone, Weaviate, Qdrant, Milvus, pgvector comparison table, decision framework, cost, KVKK, and the 'start with pgvector' advice.
Not 'which one' but 'which fits me.' Real benchmark data, a decision framework, the recall-latency tradeoff, and KVKK/data residency. Synthetic benchmarks lie.
Choosing a vector database depends on scale, latency and operations. I compare pgvector, Qdrant, Milvus and Pinecone with 2026 benchmarks and a decision framework.
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.
pgvector or Qdrant? A 2026 production comparison on latency, hybrid search, and scale, with RAG evaluation metrics and self-hosted options for KVKK.
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.
Vector database selection 2026: pgvector, Qdrant, Milvus compared. Latency, throughput, scalability, and KVKK-compliant hosting. Start small, benchmark with your own data, grow.
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 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.
On multi-hop questions GraphRAG scores 86%, vector RAG 32%. But the right answer for most is a hybrid router. An architecture decision framework in a Turkish/KVKK context.
Which vector database is right for RAG and agents? I compare pgvector, Qdrant, Milvus, and Weaviate on performance, scale, hybrid search, and data sovereignty.
For enterprise RAG in 2026, pgvector or a dedicated solution like Pinecone, Qdrant, Weaviate, Milvus? A field-tested decision guide through the lens of scale, cost, hybrid search and data sovereignty.
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.
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.