RAG (Retrieval-Augmented Generation) Architecture
RAG (Retrieval-Augmented Generation) is an architecture that grounds large-language-model answers in chunks retrieved from the organization's own documents or data sources, providing both freshness and citations.
- RAG (Retrieval-Augmented Generation) Architecture
- RAG (Retrieval-Augmented Generation) is an architecture that grounds large-language-model answers in chunks retrieved from the organization's own documents or data sources, providing both freshness and citations.
- Wikidata: Q121276171
What you will learn in this pillar
- 01Chunking strategies (semantic, recursive, document-aware)
- 02Embedding model selection (OpenAI, Cohere, BGE, multilingual)
- 03Hybrid search and re-ranking
- 04Vector databases (Qdrant, Weaviate, pgvector, Milvus)
- 05Evaluation with RAGAS
- 06Agentic RAG and graph-RAG patterns
In-depth Explanation
Blog posts on this pillar
RAG (Retrieval-Augmented Generation) Production Guide: End-to-End Architecture for Turkish Enterprises
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.
RAG (Retrieval-Augmented Generation) Production Guide: End-to-End Architecture for Turkish Enterprises →
Vector Database Comparison: Qdrant, Milvus, Weaviate, pgvector
Vector database comparison: we evaluate Qdrant, Milvus, Weaviate, and pgvector for enterprise RAG in terms of scale, performance, cost, data sovereignty, and benchmarking.
Vector Database Comparison: Qdrant, Milvus, Weaviate, pgvector →
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.
Chunking Strategies: Best Practices for Document Splitting in RAG →
Vector Database Comparison 2026: Qdrant, Pinecone, Weaviate, Milvus and pgvector
We compare Qdrant, Pinecone, Weaviate, Milvus and pgvector on scale, latency, hybrid search and KVKK. A 2026 decision flow, table, and selection checklist.
Vector Database Comparison 2026: Qdrant, Pinecone, Weaviate, Milvus and pgvector →
What Is a Vector Database? A Guide to Semantic Search and Embeddings
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 a Vector Database? A Guide to Semantic Search and Embeddings →
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.
How to Design an Enterprise RAG System: A Guide to Chunking, Embeddings, Retrieval, and Reranking →
Learning content
RAG Mimarisi 101: Niçin, Ne Zaman, Nasıl?
Retrieval-Augmented Generation: LLM'i kendi belgelerinle besleme. Mimari, faydalar, fine-tuning ile karşılaştırma.
RAG Mimarisi 101: Niçin, Ne Zaman, Nasıl? →
Project: RAG Document Q&A System
RAG over company docs: chunking, embedding, retrieval, re-ranking, anchored answers.
Project: RAG Document Q&A System →
Related training
Frequently Asked Questions
Can fine-tuning replace RAG?▾
No — fine-tuning teaches style and task behavior; it is not suited for fresh or changing knowledge. RAG is for answering from documents; fine-tuning is for shaping behavior. They complement each other.
Which vector database should I choose?▾
For a fresh POC, pgvector is enough if Postgres is already present. For production scale, Qdrant or Weaviate are recommended. Self-hosted Qdrant works well for closed-network requirements with strong filtering.
What chunk size is best?▾
Sensible default: 512-token chunks with 50–100 token overlap. Content structure dictates the rest — document-aware chunking helps with tables/code; conversation-style content often benefits from sub-256 chunks.
Is it possible to eliminate hallucinations?▾
Not entirely — but it can be brought below 5%. The practical formula: hybrid search + re-ranker + a 'do not answer without citing the source' prompt + continuous RAGAS evaluation.
How to handle multilingual RAG?▾
Use a multilingual embedding model (Cohere multilingual or BGE-M3), a language-consistent re-ranker, and locale-aware query rewriting. For Turkish content, BAAI/bge-m3 and Cohere embed-multilingual-v3 are strong baselines.
How should a RAG evaluation set be prepared?▾
Build 50–200 question / gold-answer pairs with a domain expert. Apply RAGAS metrics (faithfulness, answer relevancy, context precision/recall); run a minimal eval per PR in CI and a full eval nightly.
Other pillar topics
Enterprise AI Consulting
Enterprise AI consulting is the end-to-end discipline that takes AI from business objectives to technical architecture, prioritizing use-cases and shaping a production-ready roadmap so AI scales sustainably inside the organization.
Agentic AI and Autonomous Systems
Agentic AI is the architecture in which a large language model — instead of producing a single answer — autonomously completes multi-step tasks by combining planning, tool use, memory and feedback loops.
LLMOps: Production-Grade LLM Operations
LLMOps is the engineering discipline that covers the development, deployment, monitoring, evaluation and cost management of LLM-powered applications — extending classic MLOps with prompt versioning, eval-driven CI and observability tailored for non-deterministic systems.
AI Governance and EU AI Act Compliance
AI Governance is the corporate framework that ensures AI systems — from design to use — meet ethical, safety, transparency, explainability and legal-compliance requirements (EU AI Act, GDPR/KVKK, ISO 42001).
Corporate AI Training
Corporate AI training is a structured program — calibrated to different role levels from executives to engineers — that builds AI capability through hands-on, scenario-grounded learning with measurable outcomes.
Industry AI Use Cases
AI use cases are a pragmatic decision guide — across banking, healthcare, retail, public sector and beyond — capturing the concrete business value, success metrics and reference architectures that make AI worth building.
Prompt and Context Engineering
Prompt engineering is the applied discipline of designing instructions, examples, context and output controls so that an LLM produces consistent, accurate and cost-efficient outputs.
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