7 posts
What is hybrid search? A method that combines semantic (vector) search with BM25 keyword search, fusing scores to raise retrieval quality and hit rate in RAG systems.
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.
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.
Naive RAG's six fatal weaknesses are fully solved in 2026 by agentic RAG. A production-grade RAG with plan/reflect/verify loops, hybrid retrieval, and claim-verification built on the LangGraph v0.4 state-graph used by Klarna, LinkedIn, and Uber — plus a KVKK-compliant Turkish bank case study and cost-latency tradeoffs.
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.
In many RAG systems, quality problems come not from the language model itself but from retrieval. Wrong chunks, outdated documents, missed exact-match queries, or poorly interpreted user intent can push even strong models toward weak or misleading answers. This guide explains three of the most effective ways to improve RAG quality in production: hybrid search, metadata filtering, and query rewriting. It covers the technical rationale, enterprise use cases, common mistakes, and practical design strategies for building more reliable retrieval pipelines.
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.