The hidden cost of vector database pricing models
For a long time, usage-based pricing seemed like the safest way to run new infrastructure. The appeal...
#RAG
686 articles tagged with RAG
I built a PDF parser that actually preserves table structure for RAG — here's why it matters
Every RAG tutorial shows the same pipeline: PDF → extract text → split every 512 tokens → embed →...
GraphRAG vs vector RAG: when the knowledge graph pays for itself
When GraphRAG beats vector RAG, the 1000x indexing cost catch, and how to decide between GraphRAG, LazyGraphRAG, and hybrid retrieval.
Why production RAG fails — and the boring metrics that fix it
Diagnose retrieval failures, measure recall as its own metric, and add a cross-encoder reranker to a LangChain + FAISS RAG pipeline.
RAG Evaluation with RAGAS: Measuring Faithfulness, Context Precision, and Recall in Production
Key takeaways: RAGAS gives you four core metrics that split RAG failures into retrieval vs....
RAG vs. Fine-Tuning – The Question Every AI Builder Gets Wrong이 글은 AI 개발자들이 자주 오해하는 RAG(검색 증강 생성)와 파인튜닝의 차이를 명확히 설명한다. 파...
RAG vs. Fine-Tuning – The Question Every AI Builder Gets Wrong이 글은 AI 개발자들이 자주 오해하는 RAG(검색 증강 생성)와 파인튜닝의 차이를 명확히 설명한다. 파인튜닝은 모델 내부에 도메인 특화 행동과 지식을 내장하는 방식으로, 특정 분야의 일관된 행동과 추론에 적합하...
Chunking for RAG: stop tuning the wrong knob
A practical chunking playbook for RAG: why semantic splitters disappoint, what chunk size + overlap actually buy you, and a small eval harness in Python.
Chunking in RAG: why your splitter matters more than your embedding model
Why semantic chunkers rarely beat tuned recursive splitters, and how Anthropic's contextual retrieval cuts failed lookups by 35-67%.
RAG в enterprise: 70-80% проблем не в модели, а в данныхЭта статья родилась из работы надhttps://habr.com/ru/companies/a...
RAG в enterprise: 70-80% проблем не в модели, а в данныхЭта статья родилась из работы надhttps://habr.com/ru/companies/alpinadigital/articles/1036196/#RAG #enterprise_AI #retrieval...
Spring AI Explained — ChatClient, RAG, Advisors, and Every Core Component
Most Spring AI tutorials jump straight to code. You copy the dependency, paste the config, call...
RAG Series (19): Incremental Updates — Keeping the Knowledge Base Fresh
Knowledge Bases Are Not Static Every article in this series so far has shared one implicit...
Engineering RAG Systems That Actually Work: Conversational Retrieval, Page Awareness & Debugging (Part 5)
From a working prototype to something that actually behaves like a real system. ...
How to Build a RAG Evaluation Framework That Catches Real Problems
Six months into running a production RAG system, I had a problem: my users kept complaining about...
PKM vs RAG vs Wiki vs Memory Systems Explained Clearly
PKM, RAG, wikis, and AI memory systems are often discussed as if they solve the same problem. They do...
RAG retrieves fragments on demand. LLM Wiki compiles structured knowledge before any question is asked. Learn when inges...
RAG retrieves fragments on demand. LLM Wiki compiles structured knowledge before any question is asked. Learn when ingest-time synthesis beats query-time retrieval, and when it doe...
Designing a Production-Oriented RAG System for Technical Documentation
Large Language Models are incredibly powerful, but they have a major limitation: They do not...
RAG Series (18): Conversational RAG — The Pronoun Problem in Multi-Turn Dialogue
The Hidden Assumption in Single-Turn RAG Every article in this series so far has worked...
RAG Series (17): Agentic RAG — Giving the Agent Control Over Retrieval
The Silent Failure of Pipeline RAG Every article in this series has been trying to answer...
IITM Pravartak and Emeritus Launch Advanced Certificate Programme in Agentic AI and RAG Engineering
(MENAFN - The Mavericks) Mumbai, May 2026: Artificial intelligence is moving from experimentation to real-world deployment. Enterprises now need systems that can act, reason, and m...
Cómo construí un sistema RAG para convertirme en Red Teamer con IA — proyecto en Evolve
Llevo meses trabajando en algo que empezó como una necesidad práctica y se ha convertido en la base...
How I Beat Standard RAG by 3.5x Using TigerGraph — Building SavannaFlow
How I Beat Standard RAG by 3.5x Using TigerGraph — Building SavannaFlow TL;DR: I built a...
I Built a RAG Pipeline From Scratch and It Completely Changed How I Think About AI
I Built a RAG Pipeline From Scratch and It Completely Changed How I Think About AI I've...
📰 2026 Guide: How the Milvus Vector Database Powers Next-Gen AI Agents & Dual-Memory SystemsThe open-source Milvus vecto...
📰 2026 Guide: How the Milvus Vector Database Powers Next-Gen AI Agents & Dual-Memory SystemsThe open-source Milvus vector database, with over 44,000 GitHub stars, is becoming a fou...