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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 14, 2026 · Artificial Intelligence

Designing High‑Quality Tools for Deep Research Agents: From Search to Python Execution

This article explains how to turn simple API calls into robust, noise‑filtering tools—Search, Visit, Scholar, and Python—by adding domain blacklists, relevance scoring, query‑driven extraction, safety sandboxes, and a unified registry, ultimately boosting the success rate of LLM‑driven research agents.

AI agentsLLM safetyReAct

0 likes · 32 min read

Designing High‑Quality Tools for Deep Research Agents: From Search to Python Execution

Wu Shixiong's Large Model Academy

Apr 13, 2026 · Artificial Intelligence

Turning ReAct from Demo to Production: Handling Failures, Loops, and Token Budgets

This article explains how to upgrade a ReAct agent from a proof‑of‑concept to a production‑ready system by classifying tool failures, detecting repeated search loops, managing token budgets, and adding structured logging, complete with Python implementations and practical interview guidance.

Agent EngineeringLLMLoop Detection

0 likes · 24 min read

Turning ReAct from Demo to Production: Handling Failures, Loops, and Token Budgets

Wu Shixiong's Large Model Academy

Apr 10, 2026 · Artificial Intelligence

How to Build a Robust Agent Memory System: Architecture, Management, and Evaluation

This article provides a comprehensive guide to designing, implementing, and evaluating an Agent Memory module for large‑language‑model assistants, covering memory types, short‑ and long‑term storage, conflict resolution, hybrid retrieval, compliance, and practical interview answers.

Agent MemoryHybrid RetrievalInterview preparation

0 likes · 32 min read

How to Build a Robust Agent Memory System: Architecture, Management, and Evaluation

Wu Shixiong's Large Model Academy

Apr 9, 2026 · Artificial Intelligence

How to Jump‑Start a RAG System Without Any Labeled Data

Building a Retrieval‑Augmented Generation (RAG) system from scratch without existing QA pairs requires a systematic cold‑start approach that creates synthetic QA data, establishes baseline metrics, iteratively improves via expert labeling and real user feedback, and ensures document quality for reliable evaluation.

AnnotationEvaluation MetricsLLM

0 likes · 17 min read

How to Jump‑Start a RAG System Without Any Labeled Data

Wu Shixiong's Large Model Academy

Apr 8, 2026 · Artificial Intelligence

From RAG to Deep Research Agent: Building a Multi‑Round AI Agent with ReAct

This article walks through the practical differences between simple Retrieval‑Augmented Generation and a full Deep Research Agent, explains the four pillars that support such agents, demonstrates a minimal ReAct implementation with robust error handling, and shares interview tips for showcasing these systems.

LLMRAGTool Integration

0 likes · 18 min read

From RAG to Deep Research Agent: Building a Multi‑Round AI Agent with ReAct

Wu Shixiong's Large Model Academy

Apr 7, 2026 · Artificial Intelligence

Why Hybrid Retrieval Beats Pure Vector Search: BM25, RRF, and Real‑World Experiments

This article dissects the shortcomings of pure vector retrieval, explains how BM25 complements it, compares weighted‑sum and Reciprocal Rank Fusion (RRF) strategies, shows experimental results that identify optimal weight and k values, and provides practical engineering tips for deploying hybrid search in RAG systems.

BM25Hybrid RetrievalParameter Tuning

0 likes · 24 min read

Why Hybrid Retrieval Beats Pure Vector Search: BM25, RRF, and Real‑World Experiments

Wu Shixiong's Large Model Academy

Apr 6, 2026 · Artificial Intelligence

Why Rerank Beats Simple Retrieval in RAG: Practical Tips & Code

This article explains the limitations of Bi‑Encoder retrieval, introduces Cross‑Encoder rerankers, shows how a cascade of recall‑rerank‑generation improves answer quality, and provides concrete code, threshold‑filtering strategies, and domain‑specific fine‑tuning techniques for industrial RAG systems.

AI retrievalBi-EncoderCross-Encoder

0 likes · 20 min read

Why Rerank Beats Simple Retrieval in RAG: Practical Tips & Code

Wu Shixiong's Large Model Academy

Apr 3, 2026 · Artificial Intelligence

Why Post‑Filtering Fails in Enterprise RAG and How to Securely Pre‑Filter

Enterprise RAG systems often mistakenly apply post‑filtering, retrieving unauthorized documents before permission checks, which violates audit compliance, wastes Top‑K slots, and risks data leakage in multi‑tenant environments; this article explains why pre‑filtering at the vector search layer, proper metadata design, token validation, and dynamic permission handling are essential.

Multi‑TenantPermission ControlPre-filtering

0 likes · 15 min read

Why Post‑Filtering Fails in Enterprise RAG and How to Securely Pre‑Filter

Wu Shixiong's Large Model Academy

Apr 2, 2026 · Artificial Intelligence

How Smart Chunk Splitting Boosts RAG Recall from 67% to 91%

This article examines the critical role of chunk splitting in Retrieval‑Augmented Generation systems, comparing three generations of methods—from fixed‑size token cuts to sentence‑aware and semantic‑aware strategies—showing how refined chunking, overlap tuning, and metadata design raise Recall@5 from 0.67 to 0.91 while addressing table, list, and long‑section challenges.

ChunkingInformation RetrievalLLM

0 likes · 24 min read

How Smart Chunk Splitting Boosts RAG Recall from 67% to 91%

Wu Shixiong's Large Model Academy

Apr 1, 2026 · Artificial Intelligence

How to Design an Effective Agent Memory System for Enterprise AI Assistants

This article explains why AI agents need a structured memory module, outlines three memory types from cognitive science, details short‑term and long‑term storage architectures using vector databases, and provides concrete code and management strategies—including conflict resolution, TTL expiration, and privacy compliance—to build a robust Agent Memory system.

Agent MemoryLLMMem0

0 likes · 23 min read

How to Design an Effective Agent Memory System for Enterprise AI Assistants