Tag

#retrieval

12 posts

Late Chunking and Contextual Retrieval: 2026 Techniques That Raise RAG Accuracy

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.

15 min

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yapay-zeka

Agentic RAG Architecture Patterns: From Router to Self-RAG (2026)

Agentic RAG is not ordinary RAG. I explain the router, ReAct, plan-execute, multi-agent retrieval and self-RAG patterns, and when to choose each.

15 min

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yapay-zeka

Chunking Strategies: Best Practices for Document Splitting in RAG

What are chunking strategies? Best practices for document splitting in RAG: chunk size, overlap, semantic chunking, and structure-aware methods, end to end.

32 min

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yapay-zeka

Late Chunking and Contextual Retrieval: The 2026 RAG Chunking Playbook

The 2026 chunking strategy with late chunking, contextual retrieval, and agentic RAG. Which pipeline for which query? A production-oriented decision guide.

14 min

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yapay-zeka

How to Build a RAG Architecture? A Production Guide from Chunking to Eval

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.

34 min

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yapay-zeka

Why RAG Breaks in Production: Retrieval Failures and Adaptive Routing (2026)

In production, 73% of RAG failures come from retrieval. Hybrid search, reranking, and adaptive routing by query complexity for resilient RAG.

15 min

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yapay-zeka

What Is GraphRAG? Choosing Between Vector RAG and a Hybrid Architecture

What is GraphRAG? A RAG architecture that feeds a language model over a knowledge graph instead of flat text chunks. The difference from vector RAG, the hybrid architecture decision, entity and relationship extraction, graph-based retrieval, cost, and a decision framework.

32 min

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yapay-zeka

GraphRAG or Vector RAG? Choosing the Right Retrieval Architecture for Production in 2026

GraphRAG or vector RAG? A 2026 decision guide for production retrieval: cost, multi-hop reasoning, hybrid search, and a KVKK-aware view.

13 min

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blog-dogal-dil-isleme

Data, Morphology, and Evaluation Challenges in Turkish NLP Projects

Turkish NLP projects may look similar to general natural language processing tasks on the surface, but they involve distinct challenges in data, morphology, and evaluation. Agglutinative structure, rich inflection, surface-form explosion, the semantic role of suffixes, spelling variation, colloquial usage, code-switching, domain-specific terminology, and limited high-quality datasets make Turkish NLP much more than a simple “collect more data” problem. In addition, evaluation in Turkish NLP is often misleading when reduced to standard metrics alone, because token-level accuracy, task success, morphological correctness, rare-case performance, and production robustness are not the same thing. This guide explains the major data, morphology, and evaluation challenges in Turkish NLP projects and presents practical solution strategies across classification, NER, retrieval, LLM, and enterprise NLP settings.

30 min

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blog-dogal-dil-isleme

Enterprise NLP Use Cases: Document Processing, Review Analysis, Information Extraction, and Search

Enterprise NLP is not limited to text classification or chatbot development. Today, organizations use natural language processing across document understanding, contract and policy analysis, customer review intelligence, email and request classification, structured information extraction from unstructured text, enterprise search, knowledge access, support operations, and decision-support systems. But successful enterprise NLP systems do not emerge from model choice alone. They depend on a well-defined use case, data quality, human oversight, retrieval design, output structure, security, evaluation, and workflow integration. This guide examines enterprise NLP through four major use-case families: document processing, review analysis, information extraction, and search. For each, it explains business value, technical architecture, common failure patterns, modeling options, and practical implementation strategy.

31 min

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blog-prompt-muhendisligi

What to Do When Prompt Engineering Is Not Enough: When You Need Workflows, Retrieval, and Tool Use

Many organizations turn their first successful experiences with large language models into the mistaken belief that prompt engineering can solve every problem. In reality, while prompt design is a powerful starting point, not every task can be solved by writing better instructions. Multi-step processes require workflows, up-to-date and organization-specific knowledge requires retrieval, and interactions with systems, data sources, or business actions require tool use. This guide explains the limits of prompt engineering in enterprise settings, clarifies when prompting is enough, and shows when workflows, retrieval, or tool use become necessary—and how these layers should work together in production-grade systems.

26 min

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rag-ve-bilgi-sistemleri

How to Design an Enterprise RAG System: A Guide to Chunking, Embeddings, Retrieval, and Reranking

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.

22 min