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 →
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 →
What is an LLM? How Large Language Models Work — 2026 Reference
How do Large Language Models (LLMs) work, what does Transformer architecture solve, what are tokens, embeddings, and context windows, and how do GPT-5, Claude Opus 4.7, Gemini 3, and Llama 4 compare? A comprehensive 2026 reference covering Turkish LLM performance, training stages, hallucination control, and cost modeling.
What is an LLM? How Large Language Models Work — 2026 Reference →
Replace Classic RAG with Agentic RAG in 2026: Production Architecture on LangGraph
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.
Replace Classic RAG with Agentic RAG in 2026: Production Architecture on LangGraph →
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.
What to Do When Prompt Engineering Is Not Enough: When You Need Workflows, Retrieval, and Tool Use →
From Zero to AI Engineer in 2026: 12 Months, 5 Production-Level Projects, $200K+ Job Offer
A concrete roadmap to land a global remote AI Engineer position from zero in 12 months: 5 production-level projects, GitHub portfolio + blog strategy, $200K+ offer. Karpathy, Raschka, 3Blue1Brown, Andrew Ng curriculum; HuggingFace + LangChain + Anthropic Academy free programs; Turkish alternatives; case study (14-month timeline); and interview strategy for top offers.
From Zero to AI Engineer in 2026: 12 Months, 5 Production-Level Projects, $200K+ Job Offer →
Learning content
Project: RAG Document Q&A System
RAG over company docs: chunking, embedding, retrieval, re-ranking, anchored answers.
Project: RAG Document Q&A System →
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? →
Memory, State, and Long-Term Context
Memory layers in multi-step / multi-session agents: scratch, episodic, semantic, user profile.
Memory, State, and Long-Term Context →
Hands-on: Mini RAG — Şirket El Kitabı Q&A Botu
Sıfırdan çalışan mini RAG: Markdown belgeden chunk → embed → Postgres pgvector → retrieval → Claude yanıt.
Hands-on: Mini RAG — Şirket El Kitabı Q&A Botu →
Chunking Stratejileri: Sabit · Recursive · Semantic · Document-Aware
Belgeleri parçalara nasıl bölersin? Sabit boyut, recursive, semantic, document-aware (markdown, code) chunking. Boyut + örtüşme.
Chunking Stratejileri: Sabit · Recursive · Semantic · Document-Aware →
Modern AI: LLM'ler, Transformerlar ve Agentic Sistemler
ChatGPT'den (Kasım 2022) bugüne yapay zekânın yüzü değişti. Bu derste modern üretken AI'nin temel taşı olan transformer mimarisini, LLM'lerin nasıl eğitildiğini, prompt engineering ile RAG'in pratiğini, fine-tuning ne zaman doğru seçim olduğunu ve 2025-2026'nın ana akımı haline gelen agentic sistemleri uçtan uca öğreneceksiniz.
Modern AI: LLM'ler, Transformerlar ve Agentic Sistemler →
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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