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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

100 recent articles max
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Jul 23, 2026 · Artificial Intelligence

How to Prevent RAG from Hallucinating When No Answer Exists – Beyond Simple Similarity Thresholds

The article explains why a plain similarity‑threshold check cannot reliably stop Retrieval‑Augmented Generation from fabricating answers, introduces a four‑stage evidence‑control framework, details how to calibrate thresholds with balanced positive and negative samples, and outlines concrete actions for handling insufficient evidence.

LLMRAGanswerability
0 likes · 21 min read
How to Prevent RAG from Hallucinating When No Answer Exists – Beyond Simple Similarity Thresholds
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Jul 22, 2026 · Artificial Intelligence

How to Handle Long Conversation History: Beyond Full Prompt or Recent Rounds

The article explains that effective conversation memory for LLMs requires classifying information into static knowledge, short‑term context, and long‑term memory, defining a full lifecycle for each entry, and implementing strict storage, retrieval, update, and deletion policies rather than simply concatenating all history or keeping only the latest turns.

LLMMemory LifecycleRAG
0 likes · 24 min read
How to Handle Long Conversation History: Beyond Full Prompt or Recent Rounds
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Jul 21, 2026 · Artificial Intelligence

How to Decompose a Production‑Ready RAG System for Interview Success

The article outlines a production‑ready RAG architecture by separating offline ingestion and online query pipelines, detailing nine ingestion steps, online request flow, data storage responsibilities, failure‑handling, monitoring, and acceptance criteria, all illustrated with concrete examples and traceable state machines.

RAGfailure handlingmonitoring
0 likes · 29 min read
How to Decompose a Production‑Ready RAG System for Interview Success
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.

AIClaudeSession
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 CodeInterview Preparationauto-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