#RAG
318 related articles
Building an AI Customer Service Agent with n8n + RAG: Automated Lead Handling in Practice
Learn how to build an AI customer service agent using n8n and RAG to automate lead handling, qualify prospects, update CRM, and schedule appointments.
Getting Started with RAG Knowledge Bases: From Vector Retrieval to Embedding Models Explained
A beginner's guide to building RAG knowledge bases — covering vector retrieval, context assembly, Embedding models, and why model consistency is critical for accurate semantic search.
How Production RAG Systems Handle Complex PDFs Without Losing Document Structure
How production RAG systems handle complex PDFs without losing structure. A deep dive into layered pipeline architecture: document parsing, structure-aware chunking, and metadata enrichment.
Brute-Force Context Stuffing vs. RAG: Is Vector DB a Dead End for Agent Memory?
A developer ditched vector DBs and stuffed full JSON action logs into million-token context windows, dramatically improving agent reasoning. Is RAG a dead end for agent memory?
A New Approach to RAG Access Control: How Amazon Quick and Bedrock Protect Enterprise Data Permissions
Amazon Quick and Bedrock Knowledge Bases deliver real-time, query-time permission verification for enterprise RAG, enforcing document-level access control against SharePoint, Google Drive, and more.
EmbeddingGemma 2 Hands-On: A Multimodal Retrieval Model That Runs on Your Phone
Google's open-source EmbeddingGemma 2 maps text, images, video, and audio into one vector space — small enough to run on a phone. We break down the architecture and benchmark multimodal retrieval in Colab.
AI Chatbot Has a Knowledge Base but Still Gets It Wrong? Here's the Real Problem with RAG Retrieval
AI chatbots with knowledge bases still give wrong answers because RAG retrieves semantically similar but condition-mismatched content. Learn how metadata, condition-first retrieval, and conflict arbitration fix this.
RAG Retrieval-Augmented Generation in Practice: Building an Enterprise Private Knowledge Base from Scratch
A practical RAG tutorial covering the full pipeline for building an enterprise private knowledge base: document processing, chunking, Embedding, Milvus vector search, and LLM generation.
PageIndex: A New Reasoning-Based RAG Approach Without Vectors
PageIndex is an open-source vectorless, reasoning-based RAG framework that navigates documents via hierarchical outlines instead of vector search. 38K+ GitHub stars.
Hierarchy-Aware RAG Chunking Toolkit: A Private Deployment Solution Built for Legal Documents
A hierarchy-aware RAG chunking toolkit with legal cross-reference extraction scripts, supporting private on-premise deployment and full source code delivery. Optimized for structured documents like legal texts, compatible with LangChain.
Enterprise RAG with LLMs: Building a Q&A System Architecture from Scratch
Week 1 of an enterprise RAG course: build a Q&A system with FastAPI, unify local and online LLM calls, deploy on Linux via PyCharm remote, and validate with Postman.
Building an Enterprise RAG Knowledge Base from Scratch: A Complete Practical Guide
A hands-on guide to building an enterprise RAG knowledge base with Vue 3, Spring AI, and Milvus — covering chunking strategy, recall optimization, hallucination suppression, source attribution, and access control.
Multi-Agent Systems in 3 Minutes: From Stateless Models to Collaborative Architecture
Understand multi-agent AI systems in 3 minutes: from LLMs as stateless functions to agent loops, memory, RAG, orchestrator architecture, and the MCP protocol.
Chunkr: A Rust-Powered Text Chunking Library That's 20x Faster
Chunkr is an open-source Rust text chunking library supporting recursive, Markdown, and hierarchical strategies plus native PDF loading — benchmarked at 15–20x faster than LangChain and pypdf for RAG preprocessing.
LangChain + Amazon Bedrock: How Agentic Retrieval Tackles Multi-Part Questions
Learn how agentic retrieval with LangChain and Amazon Bedrock Managed Knowledge Bases handles complex multi-part queries that single-shot RAG can't, with trace event comparisons and cost analysis.
Naive RAG vs. Advanced Hybrid RAG: Two Paradigms of Retrieval-Augmented Generation
A clear comparison of Naive RAG vs. Advanced Hybrid RAG: from single-pass vector retrieval to hybrid architectures combining vector search, BM25, metadata filtering, and cross-encoder re-ranking.
Hybrid RAG Retrieval in Practice: A Complete Guide to Dense+BM25+RRF and Reranking
A deep dive into ReRankEval's hybrid RAG pipeline: how dense vector search, BM25, RRF fusion, and LLM reranking work together, evaluated with Hit Rate, MRR, and NDCG.
A New Paradigm for Contextual Embeddings: Perplexity Releases pplx-embed-v2 to Set New Benchmarks
Perplexity releases pplx-embed-v2-context-9b, a contextual embedding model that encodes chunks with full-document visibility, achieving SOTA on ConTEB and turbopuffer's context-bench benchmarks.
Production-Grade Agentic RAG in Practice: An Open-Source Course from Retrieval to Intelligent Agents
The open-source project jamwithai/production-agentic-rag-course focuses on production-grade Agentic RAG, with nearly 10K Stars. Learn about its core concepts and who it's for.
RAG Advanced Learning Guide: A Resource Roadmap from Prototype to Deep Mastery
A Reddit developer built a working RAG prototype but struggled to find deep, practical learning resources. This guide breaks down hybrid retrieval, document parsing, and evaluation — charting a path from 'it works' to 'I understand why.'