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AI Large Model Application Practice
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AI Large Model Application Practice

Focused on deep research and development of large-model applications. Authors of "RAG Application Development and Optimization Based on Large Models" and "MCP Principles Unveiled and Development Guide". Primarily B2B, with B2C as a supplement.

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Recent Articles

Latest from AI Large Model Application Practice

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AI Large Model Application Practice
AI Large Model Application Practice
Feb 17, 2025 · Artificial Intelligence

Mastering Structured Output for DeepSeek‑R1 with LangChain, LangGraph, and ReAct Agents

DeepSeek‑R1 excels at deep reasoning but lacks native structured output; this guide explains why structured output matters, outlines common API‑level techniques, and provides three practical solutions—using an auxiliary model with a LangChain chain, a LangGraph workflow, and a ReAct agent—complete with code snippets and JSON‑mode tips.

DeepSeekLLMLangChain
0 likes · 12 min read
Mastering Structured Output for DeepSeek‑R1 with LangChain, LangGraph, and ReAct Agents
AI Large Model Application Practice
AI Large Model Application Practice
Jan 20, 2025 · Artificial Intelligence

How Embeddings Transform Simple Character Codes into Powerful Vectors for LLMs

This article explains how embeddings convert basic character indices into high‑dimensional vectors, describes their training via gradient descent, introduces the embedding matrix, and shows how these vectors enable modern language models to capture semantic relationships and be reused across tasks.

LLMembeddingsmachine learning
0 likes · 8 min read
How Embeddings Transform Simple Character Codes into Powerful Vectors for LLMs
AI Large Model Application Practice
AI Large Model Application Practice
Jan 14, 2025 · Artificial Intelligence

Turning Classification Nets into Language Generators: A Step‑by‑Step Guide

This article explains how a simple neural network trained for classification can be adapted to generate natural language by expanding its output layer, encoding characters as numbers, using a sliding‑window context, and recursively predicting the next token, illustrating each step with diagrams and concrete examples.

AILLMlanguage generation
0 likes · 10 min read
Turning Classification Nets into Language Generators: A Step‑by‑Step Guide
AI Large Model Application Practice
AI Large Model Application Practice
Jan 9, 2025 · Artificial Intelligence

How Does Gradient Descent Train a Neural Network? A Step‑by‑Step Guide

This article walks through the complete training cycle of a simple neural network—from random weight initialization and forward propagation with labeled data, through loss calculation and gradient‑based weight updates, to iterative epochs, average loss, and practical issues like gradient explosion and vanishing.

AIModel Traininggradient descent
0 likes · 11 min read
How Does Gradient Descent Train a Neural Network? A Step‑by‑Step Guide
AI Large Model Application Practice
AI Large Model Application Practice
Jan 3, 2025 · Artificial Intelligence

How to Build an Orchestrator‑Workers AI Agent Workflow with Pydantic AI

This article explains the Orchestrator‑Workers pattern from Anthropic’s “Build effective agents”, compares it with routing and parallel modes, distinguishes it from Supervisor agents, and provides a step‑by‑step Python implementation using Pydantic AI, including model definitions, prompts, orchestration logic, worker execution, and a test example.

AI AgentsLLMOrchestrator-Workers
0 likes · 9 min read
How to Build an Orchestrator‑Workers AI Agent Workflow with Pydantic AI