RAG - aitoearn

RAG

RAG — Practical Chunking Strategies | Dewu Tech

Background In Retrieval-Augmented Generation (RAG) systems, even highly capable LLMs with repeatedly tuned prompts can still produce missing context, incorrect facts, or incoherent merges of information. While many teams keep switching retrieval algorithms and embedding models, improvements are often marginal. The real bottleneck is often a seemingly small preprocessing step

RAG

Building a RAG Application with Spring Boot, Spring AI, MongoDB Atlas Vector Search, and OpenAI

Key Takeaways * RAG overcomes LLM limitations — Retrieval‑Augmented Generation blends text generation with live information retrieval from enterprise databases, producing accurate, explainable, up‑to‑date responses. * Spring Boot + Spring AI — Provide a robust enterprise integration layer for AI, enabling multi‑provider support without disruptive codebase changes. * MongoDB Atlas vector search

Spring AI

DeepResearch Architecture and Practice Based on Spring AI Alibaba

DeepResearch System Documentation 1. Introduction & Overview DeepResearch is a Java-based intelligent research automation system built with Spring AI and Alibaba Graph. It enables an end-to-end workflow — from information gathering to analysis, and finally, structured report generation. Key Capabilities * Reasoning Chain: * Automatically constructs a logical analysis process from collected materials

RAG

Building a RAG Application with Spring Boot, Spring AI, MongoDB Atlas Vector Search, and OpenAI

# Key Takeaways - **Retrieval-Augmented Generation (RAG)** overcomes the limitations of static LLMs by combining text generation with information retrieval from enterprise databases — ensuring **accuracy, context, and transparency**. - **Spring Boot + Spring AI** streamline AI model integration into enterprise apps, supporting **multiple providers** without invasive code or infrastructure changes. - **MongoDB

LLM Memory

Memory Challenge: Why Large Language Models Sometimes Forget Your Conversations

🚀 Ditch the Vibes — Get the Context Ditch the vibes, get the context (Sponsored) Your team is shipping to production — intuition alone isn’t enough. Augment Code’s AI coding agent + industry‑leading context engine delivers production‑grade features while deeply understanding complex, enterprise‑scale codebases. With Augment, your team can:

AI memory

Want to Use AI Agents Effectively? Understand the Difference Between Short-Term and Long-Term Memory

# Understanding Short-Term vs Long-Term Memory in AI Agents In artificial intelligence, **memory** is crucial for the intelligence, coherence, and usefulness of an AI Agent. This guide explores the differences between **short-term** and **long-term** memory, and how they work together to power intelligent systems. --- ## What is an AI Agent? An

AI code review

C3 Repository AI Code Access Control Best Practices: Code Review with Qwen3-Coder + RAG

# LLM + RAG Code Review in C3-Level Repositories ## Summary This article details the **practical implementation** of an **LLM-based code review agent** in a **C3-level security** code repository. Due to strict security requirements and the prohibition of closed-source models, the solution is built on: - **Qwen3-Coder** - **RAG (Retrieval-Augmented Generation)** - **Iflow*

AI agents

Lots of Insights! Transcript of Silicon Valley’s Internal Discussion on AI Agents – Oct 2, 2025

Datawhale Insights: Why 95% of AI Agents Fail in Production At a recent Silicon Valley industry event, Oana Olteanu—a renowned venture capitalist—joined engineers and ML leads from Uber, WisdomAI, EvenUp, and Datastrato to discuss how AI Agents can succeed in production environments. Key statistic: 95% of AI Agents