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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
Jul 3, 2026 · Artificial Intelligence

Deep Research Series: 12 Articles From the Basic Loop to the First Training Review

This article reorganizes a 12‑part Deep Research Agent series into a logical learning path, summarizing each part’s problem, key solutions, and practical takeaways—from building a runnable loop and handling tool failures to data construction, context management, and training evaluation.

Context ManagementDeep ResearchLLM agent
0 likes · 12 min read
Deep Research Series: 12 Articles From the Basic Loop to the First Training Review
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Jul 2, 2026 · Artificial Intelligence

Is Changing Session, Skill, or Model the Same? Why Claude Code’s Self‑Review Fails

The article explains why Claude Code’s self‑review is inherently unreliable and distinguishes four distinct remedies—starting a new session, swapping a review Skill, changing the underlying model, and using Sub‑Agent or Worktree isolation—each solving a different problem and none interchangeable, then outlines a risk‑based workflow for AI‑assisted code review.

AIClaudeSkill
0 likes · 12 min read
Is Changing Session, Skill, or Model the Same? Why Claude Code’s Self‑Review Fails
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Jun 30, 2026 · Artificial Intelligence

Why Claude Code Seems to Forget: The Hidden Auto‑Compact Mechanism Explained

The article demystifies Claude Code's auto‑compact feature, showing how context limits trigger automatic summarization that discards most historic data, which parts survive compression, and practical strategies—including file persistence, directive‑based compaction, child agents, and proactive clearing—to keep critical information alive during long sessions and interview discussions.

Claude CodeContext Managementauto-compact
0 likes · 20 min read
Why Claude Code Seems to Forget: The Hidden Auto‑Compact Mechanism Explained
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Jun 29, 2026 · Artificial Intelligence

Why Adding an MCP Server to Claude Code Isn’t Just a Simple Plug‑In (Interview Answer Scores 60)

The article explains Claude Code's Model Context Protocol (MCP), how to connect external servers, the hidden costs and trust implications, the tool‑search lazy‑loading mechanism that prevents context overload, and the three security risks you must manage, all illustrated with real‑world examples and a four‑step interview answer.

AI integrationClaude CodeMCP
0 likes · 24 min read
Why Adding an MCP Server to Claude Code Isn’t Just a Simple Plug‑In (Interview Answer Scores 60)
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Jun 26, 2026 · Artificial Intelligence

Claude Code Hooks: How to Bypass Model Self‑Awareness and Enforce Deterministic Actions

Claude Code hooks are deterministic scripts that run at specific events—such as PreToolUse, PostToolUse, SessionStart, and UserPromptSubmit—to enforce hard constraints, bypass the probabilistic suggestions in CLAUDE.md, and enable interception, feedback, analytics, and automation, with detailed configuration, input/output protocols, and safety considerations.

AI automationClaude CodeConfiguration
0 likes · 25 min read
Claude Code Hooks: How to Bypass Model Self‑Awareness and Enforce Deterministic Actions
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Jun 24, 2026 · Artificial Intelligence

Why Public QA Datasets Fail for Deep Research Agents—and How to Build Effective Training Data

The article explains that single‑ or two‑hop QA datasets cannot teach Deep Research agents multi‑step reasoning, outlines four mainstream data‑construction methods, describes trajectory sampling with a three‑stage funnel filter, and shares practical guidelines on data volume, difficulty distribution, question types, and common pitfalls.

AI Agent TrainingData ConstructionDeep Research
0 likes · 32 min read
Why Public QA Datasets Fail for Deep Research Agents—and How to Build Effective Training Data
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Jun 23, 2026 · Artificial Intelligence

When RAG Returns Junk, Why a LLM Can’t Fix It – Building an Agentic RAG

The article examines why traditional single‑step Retrieval‑Augmented Generation fails when retrieved passages are irrelevant, outlines the three fundamental flaws of that pipeline, and presents the Agentic RAG paradigm—turning retrieval into a reusable tool with planning, reflection, and decision loops, illustrated with code, interview scenarios, and practical deployment tips.

AIAgentic RAGKnowledge Base
0 likes · 32 min read
When RAG Returns Junk, Why a LLM Can’t Fix It – Building an Agentic RAG
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Jun 20, 2026 · Artificial Intelligence

How I Burned $15K on Claude Code in a Month and Finally Mastered Skill Writing

After spending nearly $15,000 on Claude Code and Codex in a single month, the author discovered that most of his dozens of skills were never invoked, learned the progressive‑disclosure mechanism, rewrote skill descriptions, added verification steps, organized skills as folders with scripts and hooks, and now knows how to identify and optimize the truly useful skills.

AI agentsClaude CodeSkill Development
0 likes · 19 min read
How I Burned $15K on Claude Code in a Month and Finally Mastered Skill Writing