Silent Failures: Why Production RAG Systems Degrade Without Throwing ErrorsA production RAG system can pass every operational check and still fail the only test that matters: telling the truth. Requests complete, latency stays within target, and the generated prose reads conAug 21 · 17 min readahmetozel.hashnode.dev
Evaluating RAG: From Golden Datasets to LLM-as-JudgeA Retrieval-Augmented Generation system can produce a fluent answer while failing in several independent ways. It may retrieve the wrong evidence, omit a required document, misread correct context, anAug 10 · 15 min readahmetozel.hashnode.dev
Beyond Vector Search: Knowledge Graphs, Structured Retrieval, and Intelligent RoutingVector search is an effective way to retrieve passages that are semantically similar to a question. It is not a universal interface to every kind of knowledge. Exact identifiers, deterministic calcula1Aug 5 · 30 min readahmetozel.hashnode.dev
The Anatomy of Production-Grade RetrievalRetrieval-Augmented Generation is often summarized as a three-step workflow: split documents, embed the chunks, and send the nearest results to a language model. That description is useful for a proto1Jul 21 · 34 min readahmetozel.hashnode.dev
Building a Local-First OCR + LLM Pipeline for Structured Business DocumentsTurning a scanned business document into reliable structured data is not a single-model problem. Optical character recognition is only one stage in a longer system that must handle image quality, page14MJul 16 · 40 min readahmetozel.hashnode.dev
