125 related articles

Build high-quality AI projects on a budget. Learn how to use Ollama, Groq, Chroma, and other free open-source tools to build RAG systems and multi-Agent workflows from scratch.

Build high-quality AI projects on a budget. Learn how to use Ollama, Groq, Chroma, and other free open-source tools to build RAG systems and multi-Agent workflows from scratch.

A systematic guide to the complete learning path for AI Agent development—covering prompt engineering, RAG knowledge bases, LangChain & LangGraph, fine-tuning, and multi-agent collaboration.

A beginner-friendly guide to AI Agent development, covering the full learning path from LLM fundamentals, prompt engineering, and RAG to LangChain and multi-agent collaboration.

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.

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.

A deep dive into the DeepLearning.AI & Neo4j course 'Knowledge Graphs for RAG' — covering core concepts, vector retrieval synergy, and hands-on SEC filing demos.

A deep dive into two enterprise RAG knowledge isolation strategies: physical isolation vs. adaptive soft boundaries — covering metadata tagging, dynamic user-profile filtering, hybrid retrieval architecture, and data quality best practices.

Gaurav Sen reveals the fatal trap in AI learning: starting from ML fundamentals often leads to burnout. Learn the Onion Model approach—RAG, Agents first, Transformers next, math last.

A complete guide to Java AI development: Spring AI, LangChain4j, Spring AI Alibaba, and AgentScope4j — framework comparisons, selection tips, and a clear learning path.

Build an AI game assistant from scratch with no coding experience! This hands-on guide walks you through Dify + RAG — from knowledge base setup to agent creation and tuning.
Building AI Engineering Skills from Sc…
A deep dive into 'ai-engineering-from-scratch,' the GitHub project with 38K+ stars that helps developers build real AI engineering skills through a Learn-Build-Ship methodology.

A deep dive into n8n: 500+ integration nodes, low-code visual workflows, and AI capabilities. How it compares to Dify and Coze, plus China localization challenges.

A complete guide to installing and configuring OpenAI Codex desktop and CLI clients, covering model settings, API relay integration, prompt caching, and real cost data for GPT-5.6 AI coding.

An in-depth look at the core tech behind AI Agents: how the HNSW, IVF, and PQ vector search algorithms power RAG and long-term memory. Understand where a model's "memory" and "knowledge" come from.

A complete 5-stage AI large model learning roadmap — from Python basics and prompt engineering to RAG pipelines, Agent development, and private model deployment.

Learn LangChain 1.3 core concepts including LLM model abstraction, RAG retrieval-augmented generation, and Agent orchestration. Build a Deep Agent with planners, tools, and reflection modules.

A hands-on guide to building a local AI agent and private knowledge base using Cherry Studio, MCP, and Ollama — with web scraping, report generation, and terminal control.
Java Local LLM Inference: Low-Latency …
Learn how Java and OpenJDK Panama FFM API enable local LLM inference. Explore the technical foundations, JVM ecosystem benefits, and low-latency AI deployment in enterprise Java systems.

Spring AI is Java's answer to LangChain — offering unified multi-model APIs, structured output, RAG, Tool Calling, and MCP protocol support for enterprise LLM development.