#Cross-Encoder reranking

46 related articles

Jul 28, 2026·4 min

Spring AI 2.0 in Practice: Building an Enterprise RAG Knowledge Base Q&A System with Cursor

Build an enterprise RAG knowledge base Q&A system using Spring AI 2.0, Cursor AI programming, Ollama local deployment, and Redis vector storage. Runs on just 4GB VRAM.

Jul 27, 2026·4 min

The More Complex the AI Agent, the More It Hallucinates? The Hidden Cost of Architectural Complexity

Why do AI Agents hallucinate more as they grow more complex? This article analyzes the causes from error accumulation, context noise, and model completion nature, with 5 practical production strategies.

Jul 24, 2026·4 min

Spring AI 1.0 Practical Guide: A Java Developer's Fast Track to Building AI Applications

Spring AI 1.0 is here — Java developers can now build AI apps without switching to Python. This guide covers LLM integration, RAG, intelligent customer service, and Agent patterns for enterprise deployment.

Jul 23, 2026·8 min

LangChain Crash Course: A Complete Roadmap from Zero to Agent Development

A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.

Jul 23, 2026·7 min

Context Engineering: A Systematic Approach to Stop AI Agents from 'Talking Nonsense'

Context engineering is the core methodology for building efficient AI Agents, covering query enhancement, RAG retrieval, prompt design, memory management, and tool invocation. Master Write, Select, Compress, and Isolate to solve LLM hallucination at its root.

Jul 23, 2026·7 min

The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Topics in the Agent Domain

A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.

Jul 23, 2026·6 min

The AI Builder's Handbook: A Complete Guide to LLM Principles, RAG Architecture, and Agent Design

From the autocomplete nature of LLMs, tokens, and context windows to RAG vector databases, the MCP protocol, and AI agent loop design — this article uses vivid analogies to unpack the reality of AI engineering.

Jul 21, 2026·3 min

The Truth Behind Three RAG Pipeline Crashes: Why Inter-Stage Data Contracts Matter

A RAG pipeline crashed three times due to inter-stage data format mismatches. Learn how JSON Schema and Pydantic explicit contracts turn fragile AI pipelines into maintainable systems.

Jul 20, 2026·2 min

Agent Skill Hit Rate Dropping? 4 Methods to Solve the Skill Routing Problem

Skill hit rate dropping as your Agent scales? Learn how Progressive Disclosure works and explore 4 solutions: better descriptions, Skill Trees, negative samples, and recall + rerank.

Jul 16, 2026·3 min

Can Small Local Models Become Software Domain Experts? A RAG Architecture Deep Dive

Can small local models (1.5B–3B) become software domain experts? This article breaks down CPT, SFT, RAG, and Agent architectures, with a layered RAG-centric design for CPU-only local deployment.

Jul 12, 2026·4 min

The Ultimate RAG Interview Guide: 204 Questions, 12 Architectures, and 6 Failure Modes Explained

The open-source project "Interview System" offers 204 RAG interview questions, 12 architecture approaches, and deep analysis of 6 failure modes. Prepare systematically for RAG engineer roles.

Jul 12, 2026·4 min

The Complete AI Agent Development Learning Path: From Zero to Enterprise-Grade Practice

A systematic AI Agent development learning path covering fundamentals, prompt engineering, tool calling, multi-agent collaboration, and hands-on practice with LangChain, CrewAI, and Dify.

Jul 12, 2026·4 min

A Complete Breakdown of the Engineering Challenges Behind RAG and Agent Interview Questions

Resume full of RAG and Agent but keep failing interviews? The issue is you only run demos and can't explain production engineering challenges. This article breaks down data cleaning, hybrid retrieval, hallucination protection, and agent loop breakers.

Jul 12, 2026·6 min

The Eight-Step Method for Agent Development: A Practical Path from Pitfalls to Production

Many enterprises fail at AI Agents due to choosing the wrong tools and lacking methodology. This article outlines an eight-step Agent development method—from cognitive foundations, scenario selection, hand-writing ReAct, and structured output to Tool Use, RAG, evaluation sets, and production fallback.

Jul 12, 2026·6 min

Getting Started with AI Agents: Principles, Frameworks, and a Guide to Building Enterprise-Grade Agents

In-depth analysis of AI Agent core principles: why LLMs need Agent technology, the evolution from Prompt to RAG to Agent, Agent Tuning methods, and enterprise cost evaluation to help you build enterprise-grade agent applications.

Jul 12, 2026·4 min

AI Persistent Memory Three-Layer Architecture Explained: In-Depth Comparison of Mem0, Zep, and ContextNest

Deep dive into the three-layer architecture of AI persistent memory systems—storage, management, and retrieval—with an in-depth comparison of Mem0, Zep, and ContextNest to help developers choose the right memory solution for AI Agents.

Jul 11, 2026·4 min

Dify Getting Started: A Complete Guide to Building AI Workflows Without Code

A complete guide to Dify's core features and 1.8.0 deployment. Covers 5 app types, Docker setup, Workflow vs Chatflow differences, and RAG knowledge bases for beginners.

Jul 11, 2026·5 min

Embedding Fine-Tuning in Practice: A Core Technique for Improving RAG Retrieval Quality

Poor RAG retrieval? The root cause often lies in the Embedding model. This article explores why fine-tuning embedding models is necessary, the limits of general Embeddings, and where Embedding fine-tuning fits in RAG optimization.

Jul 11, 2026·3 min

How to Choose a Practical LLM/RAG/AI Agent Course: Four Key Criteria

Learn how to pick the best LLM, RAG, and AI Agent courses. Discover 4 key criteria for hands-on AI learning and top resources for developers.

Jul 10, 2026·7 min

Getting Started with AI Agents: A Deep Dive into the Perception-Decision-Action Loop

A clear, in-depth guide to how AI Agents work: the paradigm shift from traditional programs, the perception-decision-action loop, and the four pillars—LLMs, tool calling, memory, and RAG.