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Wu Shixiong's Large Model Academy
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Wu Shixiong's Large Model Academy

We continuously share large‑model know‑how, helping you master core skills—LLM, RAG, fine‑tuning, deployment—from zero to job offer, tailored for career‑switchers, autumn recruiters, and those seeking stable large‑model positions.

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Recent Articles

Latest from Wu Shixiong's Large Model Academy

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Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Nov 1, 2025 · Artificial Intelligence

Turn a Basic RAG Demo into a High‑Impact Interview Project

This guide shows how to evolve a simple Retrieval‑Augmented Generation prototype into a production‑grade system by strengthening data ingestion, optimizing retrieval with hybrid and reranking techniques, adding query rewriting, long‑context handling, reinforcement learning, and multimodal support, so candidates can demonstrate real engineering depth in interviews.

AILLMRAG
0 likes · 7 min read
Turn a Basic RAG Demo into a High‑Impact Interview Project
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Oct 27, 2025 · Artificial Intelligence

Designing Effective Generation Modules for RAG: Prompt Engineering, Multi‑Document Fusion, and Hallucination Control

This article explains how to design and optimize the generation module of Retrieval‑Augmented Generation systems by building robust prompts, merging multi‑source information, controlling answer formats, and applying post‑generation verification to reduce hallucinations and improve enterprise‑grade performance.

AIGeneration ModuleHallucination Control
0 likes · 9 min read
Designing Effective Generation Modules for RAG: Prompt Engineering, Multi‑Document Fusion, and Hallucination Control
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Oct 24, 2025 · Artificial Intelligence

Can Large Language Models Truly Plan? Unpacking Agent Frameworks

This article explains why most LLM‑based agents only perform pseudo‑planning through prompts or hard‑coded loops, outlines when to rely on prompt‑driven versus program‑driven planning, compares popular frameworks such as ReAct, MRKL, BabyAGI and AutoGPT, and clarifies what true autonomous planning would require.

AgentArtificial IntelligenceAutoGPT
0 likes · 12 min read
Can Large Language Models Truly Plan? Unpacking Agent Frameworks
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Oct 23, 2025 · Artificial Intelligence

Why the Transformer Core Structure Is the Key to AI Interview Success

This article explains the fundamental purpose, architecture, and variants of the Transformer model—including Encoder‑Decoder, Encoder‑only, and Decoder‑only designs—while detailing how attention mechanisms work and why modern large‑language models favor the Decoder‑only approach, providing a concise framework for answering interview questions.

AI InterviewEncoder-DecoderLarge Language Model
0 likes · 10 min read
Why the Transformer Core Structure Is the Key to AI Interview Success
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Oct 22, 2025 · Artificial Intelligence

Mastering LLM Training: A Step‑by‑Step Blueprint from Data to Alignment

This guide walks through the complete end‑to‑end process of training a large language model from scratch, covering data collection, cleaning, tokenization, pre‑training objectives and engineering, post‑training alignment methods, scaling laws, over‑fitting mitigation, and gradient‑stability techniques.

LLMPretrainingalignment
0 likes · 9 min read
Mastering LLM Training: A Step‑by‑Step Blueprint from Data to Alignment
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Sep 28, 2025 · Artificial Intelligence

Can AI Automate the Entire Research Cycle? From Paper Reading to Code Reproduction

The author builds an AI‑driven end‑to‑end assistant that transforms a research paper into a structured reading note, generates reproducible code, runs experiments, summarizes results, and creates a report, demonstrating how large language models like Kimi K2 can streamline the entire paper‑to‑implementation workflow.

AI WorkflowClaude CodeCode Generation
0 likes · 9 min read
Can AI Automate the Entire Research Cycle? From Paper Reading to Code Reproduction
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Sep 26, 2025 · Artificial Intelligence

Crack Large-Model Interviews: Master Positional Encoding, Residuals, LayerNorm & FFN

Preparing for large-model interview? This guide reveals why interviewers probe seemingly minor components—positional encoding, residual connections, layer normalization, and feed-forward networks—explains each technique's purpose, variants, and how to answer confidently, plus practical tips and a learning roadmap to boost your chances.

Artificial IntelligenceFFNInterview Tips
0 likes · 8 min read
Crack Large-Model Interviews: Master Positional Encoding, Residuals, LayerNorm & FFN