#LLM development
3372 related articles
·3 min
LangChain 1.3 in Practice: A Complete Learning Path from LLMs to Agent Development
A comprehensive guide to LangChain 1.3 — covering the full learning path from Models to Agent development, including Harness architecture, LangGraph, memory management, HITL, and Guardrails.
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·3 min
Spring AI 2.0 Deep Dive: A Java Developer's Guide to LLM Application Development
A deep dive into Spring AI 2.0: provider-agnostic APIs, RAG with vector databases, and how Java developers can build LLM apps using the Spring ecosystem.
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·3 min
AI Agent Beginner's Guide: LLMs vs. Agents, and a Full Breakdown of Development Tools
What is an AI Agent? This guide explains the key differences between LLMs and Agents, breaks down the Agent formula (LLM + Workflow + Knowledge Base), and compares tools like Dify, Coze, LangChain, and LlamaIndex.
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·3 min
Getting Started with LangChain: The Core Framework for LLM Application Development and Its Three Key Components
LangChain is the leading open-source framework for LLM application development, supporting GPT-4, GLM, and other mainstream models. This article dives into its three core concepts: Components, Chains, and Agents.
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·7 min
AI Coding in Practice: Generating a Minecraft Clone in 90 Minutes as LLMs Enter Project-Level Development
A major breakthrough in AI coding! Hands-on tests show new LLMs generating a Minecraft clone in 90 minutes and a TMNT game in 30 minutes, building 3D scenes, animation, and game logic in one shot.
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·4 min
Switching Careers to LLM Development: The Four Core Competency Layers Companies Actually Need
Want to break into LLM development but not sure where to start? This guide breaks the core skills into four progressive layers — from basic knowledge to RAG, fine-tuning, Agents, and multimodal — so you can align with real enterprise needs and land the job.
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·3 min
From Raw LLMs to Agents: A Complete Guide to Enterprise-Grade AI Development
Prompt engineering and RAG are just the basics. Real enterprise AI runs on Agents. Explore the 4 stages of LLM deployment, Agent core capabilities, and industry trends.
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·7 min
LangChain Hands-On Guide: From LLM Calls to Agent Development
Master LangChain from scratch: the three limitations of LLMs, init_chat_model unified interface config, the Message type system, and the path from LLM calls to Agent development.
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·7 min
Getting Started with OpenAI LLM Development: A Practical Guide to Model Selection, Token Pricing, and API Usage
A systematic guide to the three cores of OpenAI LLM app development: GPT-4/GPT-3.5 model selection, token billing and cost-saving tips, and practical use of the Models, Completion, and Chat Completion APIs.
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·4 min
Career Switch to LLM Development: A Four-Level Skill Progression Roadmap
Want to switch careers into LLM development but don't know where to start? This guide breaks down a four-level skill roadmap — from basics and API calls to RAG, fine-tuning, Agent development, and multimodal — to help you build real AI career value.
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·3 min
LLM Development Learning Path: From Zero to Enterprise-Level Projects
A complete LLM development learning roadmap covering prompt engineering, RAG, AI Agents, and fine-tuning — helping beginners master LangChain, LlamaIndex, and more.
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·2 min
AI LLM Development Career Path: Four Stages from Zero to $60K+/Year
A four-stage learning path for AI LLM application development: from Python basics and RAG architecture to Agent cluster orchestration, helping developers transition into AI roles.
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·2 min
LangChain + MCP Agent Development: A Practical Guide to LLM Selection and Common Pitfalls
A deep dive into LLM selection for LangChain and MCP agent development, comparing DeepSeek V3/R1 vs Qwen3 on Function Calling and MCP support with practical tips.
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·3 min
A Three-Month Roadmap to LLM Development: A Deep Dive into the Learning Path from Zero to Freelancing
A deep dive into the three-step LLM development learning path: from prompt engineering and RAG knowledge bases to AI Agent development, with realistic timelines for beginners and experienced developers.
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·3 min
LangChain from Beginner to Agent Development: A Complete Learning Path for LLM Application Development
A systematic guide to LangChain LLM application development, covering environment setup, core components (RAG, Chain, Memory), and Agent development to help developers master LLM app building.
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Tutorials·1 min
Essential Skills for LLM Engineers: A Complete Guide to Application Development and Fine-Tuning
A systematic guide to LLM engineer core skills covering RAG, Agent app development and SFT, RLHF fine-tuning, with clear learning paths for different backgrounds.
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Tutorials·3 min
Getting Started with LangChain: Core Concepts and Hands-On Guide for LLM and Agent Development
A systematic guide to LangChain's core features, covering LLM vs. Agent concepts, unified interface design, multi-provider support, environment setup, and hands-on code examples for AI app development.
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Tutorials·2 min
Career Progression in AI/LLM Application Development: Four Stages from Beginner to 40K Monthly Salary
A systematic four-stage career path for AI/LLM application development: from RAG and Agent fundamentals to architecture design, helping developers transition to AI roles targeting 40K+ monthly salary.
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Tutorials·3 min
Getting Started with LLM Application Development from Scratch: A Complete Guide to Learning Paths and Career Directions
A complete beginner's guide to LLM application development: learn the three key directions (API calling, RAG, Agent), master frameworks like LangChain, and follow a step-by-step learning path to become an AI application developer.
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Tutorials·3 min
Learning LLM Application Development from Scratch: A Complete Roadmap from RAG to Agent
How to start LLM application development from scratch? A complete roadmap covering Python basics, RAG knowledge bases, and Agent development with LangChain.
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