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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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Latest from Wu Shixiong's Large Model Academy

100 recent articles max

Wu Shixiong's Large Model Academy

Apr 28, 2026 · Artificial Intelligence

Why Bigger Context Fails for Deep Research Agents and How IterResearch Fixes It

Interviewers point out that simply enlarging the LLM’s context window cannot prevent forgetting early conclusions in long‑step Deep Research tasks; the article explains the ReAct context issues, introduces the IterResearch framework with evolving reports, and compares its accuracy, cost, and scalability against ReAct and ReSum.

Context ManagementDeep ResearchIterResearch

0 likes · 17 min read

Why Bigger Context Fails for Deep Research Agents and How IterResearch Fixes It

Wu Shixiong's Large Model Academy

Apr 27, 2026 · Artificial Intelligence

Can Your RAG Pass the Demo? Scaling to 5,000 Docs for Reliable Answers

The article walks through the practical challenges of turning a RAG demo into a production system for 5,000 insurance documents, covering knowledge‑base chunking, embedding model selection, recall‑threshold tuning, hybrid vector‑BM25 retrieval, intent‑aware query routing, prompt constraints, confidence scoring, and operational scaling, with concrete metrics and code examples.

EmbeddingHybrid RetrievalIntent Recognition

0 likes · 16 min read

Can Your RAG Pass the Demo? Scaling to 5,000 Docs for Reliable Answers

Wu Shixiong's Large Model Academy

Apr 23, 2026 · Industry Insights

Should You Take a Tencent AI Internship? Key Factors to Consider

The article examines whether a Tencent AI internship is worth pursuing by analyzing the program’s growth stage, unique user ecosystem, mentorship structure, compensation model, and early‑year advantages, illustrated with real intern case studies, to help students decide what they aim to gain from the experience.

AI internshipAI researchTech Industry

0 likes · 14 min read

Should You Take a Tencent AI Internship? Key Factors to Consider

Wu Shixiong's Large Model Academy

Apr 22, 2026 · Artificial Intelligence

How to Classify and Manage Agent Memories for Better Retrieval

This article dissects Claude Code's memory system, explains why unstructured memory degrades performance, introduces four distinct memory types with concrete examples and schema, shows how to handle expiration and retrieval strategies, and provides step‑by‑step implementation code to improve agent reliability.

Agent MemoryLLMPython

0 likes · 19 min read

How to Classify and Manage Agent Memories for Better Retrieval

Wu Shixiong's Large Model Academy

Apr 21, 2026 · Artificial Intelligence

When Should an LLM Agent Extract Memory? A Deep Dive into Trigger Strategies

The article analyzes why memory extraction in LLM‑driven agents incurs cost, compares four frameworks—Claude Code, Generative Agents, MemGPT, and Mem0—detailing their trigger mechanisms, concurrency handling, and trade‑offs, and offers practical guidance for choosing the right strategy in real‑time, social, or batch‑processing scenarios.

AI engineeringAgent designLLM

0 likes · 18 min read

When Should an LLM Agent Extract Memory? A Deep Dive into Trigger Strategies

Wu Shixiong's Large Model Academy

Apr 20, 2026 · Artificial Intelligence

Why Java Skills Alone Won’t Cut It for LLM Application Engineering

The article debunks the myth that Java developers only need a bit of AI knowledge to succeed in LLM application roles, explaining the full engineering stack—from retrieval and prompt design to deployment and performance tuning—through real‑world examples, metrics, and interview‑ready advice.

AI engineeringCareer transitionInterview preparation

0 likes · 13 min read

Why Java Skills Alone Won’t Cut It for LLM Application Engineering

Wu Shixiong's Large Model Academy

Apr 20, 2026 · Artificial Intelligence

How to Build Multi‑Step Reasoning Training Data for Deep Research Agents

Standard QA datasets fall short for deep research tasks because they lack the multi‑step, dynamic reasoning required; this article explains why, outlines four data‑construction techniques—SailorFog‑QA, WebFrontier, WebShaper, E2HQA—details trajectory sampling, filtering, scale considerations, and interview‑ready explanations.

AI agentsLLM trainingMulti-step Reasoning

0 likes · 16 min read

How to Build Multi‑Step Reasoning Training Data for Deep Research Agents

Wu Shixiong's Large Model Academy

Apr 17, 2026 · Backend Development

How Claude Code’s Memory System Works: From SHA‑256 Storage to Coalescing Extraction

This article dissects Claude Code’s Memory subsystem, explaining the distinction between Session logs and persistent Memory, the SHA‑256‑based storage layout, file indexing, four memory types, prompt injection steps, two write pathways, the ExtractionCoordinator’s coalescing strategy, and how to explain the design in interviews.

Claude CodeMemory Systembackend architecture

0 likes · 19 min read

How Claude Code’s Memory System Works: From SHA‑256 Storage to Coalescing Extraction

Wu Shixiong's Large Model Academy

Apr 16, 2026 · Interview Experience

Turn Memorized Answers into Deep Understanding for Tech Interviews

This article explains why interviewers use seemingly rote questions to probe a candidate's true grasp of concepts, contrasts memorization with genuine understanding using PPO vs GRPO, and provides a practical three‑question framework and dialogue examples to help candidates answer technical principle questions confidently.

Answering TechniquesGRPOPPO

0 likes · 12 min read

Turn Memorized Answers into Deep Understanding for Tech Interviews

Wu Shixiong's Large Model Academy

Apr 15, 2026 · Interview Experience

How to Turn Your RAG Project into a Compelling Interview Story

This article explains why many candidates fail to convey their RAG projects in interviews, contrasts tool‑list versus problem‑driven presentations, and provides a four‑question framework with concrete metrics, decision‑making examples, and actionable steps to rebuild a persuasive project narrative.

AIDecisionMakingLLM

0 likes · 16 min read

How to Turn Your RAG Project into a Compelling Interview Story