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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
Dec 11, 2025 · Artificial Intelligence

Why Reward Models Need Reasoning: From Scalar Scores to RM‑R1

Interviewers increasingly ask why modern reward models must go beyond scalar scores to incorporate reasoning, and this article explains the limitations of traditional scalar reward models, the benefits of the RM‑R1 framework, and how reasoning‑based rewards improve alignment, stability, and task performance in large language model training.

AI AlignmentLLMRLHF
0 likes · 11 min read
Why Reward Models Need Reasoning: From Scalar Scores to RM‑R1
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Dec 6, 2025 · Artificial Intelligence

How to Engineer Industrial‑Scale Function Call Data for AI Agents

This article explains why handcrafted SFT data fails for function‑call agents, introduces a controllable data‑sandbox approach, details label and variable design, shows code for seed generation and full dialogue synthesis, and demonstrates how the resulting dataset improves model routing, multi‑turn handling, tool sequencing, and error resilience.

AI-agentPrompt Designdata-engineering
0 likes · 12 min read
How to Engineer Industrial‑Scale Function Call Data for AI Agents
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Dec 5, 2025 · Artificial Intelligence

Why Do LLM Function Calls Hallucinate Parameters and How to Prevent It?

This article explains the root causes of hallucinated parameters in LLM Function Calls, outlines five common failure patterns, and presents a systematic five‑step engineering framework—including schema design, prompt rules, dynamic routing, result validation, and clarification—to reliably eliminate such errors in real‑world AI agents.

AI AgentLLMfunction call
0 likes · 11 min read
Why Do LLM Function Calls Hallucinate Parameters and How to Prevent It?
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Dec 1, 2025 · Artificial Intelligence

Why ReAct Is the Dominant Framework for Building Reliable AI Agents

The article explains why the ReAct (Reason + Act) framework outperforms simple Chain‑of‑Thought prompting by adding executable actions, environment state awareness, and feedback loops, making large language models into controllable, reproducible, and error‑recoverable agents suitable for real‑world applications and interview discussions.

Interview TipsReactagent framework
0 likes · 9 min read
Why ReAct Is the Dominant Framework for Building Reliable AI Agents
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Nov 27, 2025 · Artificial Intelligence

Why Your Enterprise AI Agent Fails and How to Fix the Four Biggest Pitfalls

This article explains why many enterprise AI agents break down in real projects, identifies four common pitfalls—including mistaking agents for chatbots, lacking schema‑level tool logic, missing memory and variable injection, and absent end‑to‑end pipelines—and offers concrete engineering solutions to build robust, task‑driven agents.

AI AgentEnd-to-End PipelineEnterprise AI
0 likes · 8 min read
Why Your Enterprise AI Agent Fails and How to Fix the Four Biggest Pitfalls
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Nov 24, 2025 · Artificial Intelligence

Why Dynamic Function Routing Is the Key to Stable LLM Agents

In real‑world LLM agents, giving the model too many tools at once leads to frequent function‑call errors, but applying dynamic function routing to narrow the candidate set dramatically reduces the error rate—from over 20% down to around 1%—and provides clear guidelines on when and how to implement it.

AgentDynamic RoutingLLM
0 likes · 9 min read
Why Dynamic Function Routing Is the Key to Stable LLM Agents
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Nov 22, 2025 · Artificial Intelligence

Why Your RAG System Slows Down Over Time and How to Fix It

The article explains why a production Retrieval‑Augmented Generation (RAG) system becomes slower as it runs—due to growing embedding costs, expanding vector databases, heavier re‑ranking, and larger prompts—and provides concrete engineering optimizations such as batching, async concurrency, caching, partitioned retrieval, HNSW tuning, replica scaling, answer caching, and prompt sparsification to keep performance stable.

AI engineeringPerformance OptimizationRAG
0 likes · 10 min read
Why Your RAG System Slows Down Over Time and How to Fix It
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Nov 21, 2025 · Artificial Intelligence

How to Build a Multi‑Layer Cache for Dynamic RAG Systems

This article explains why dynamic Retrieval‑Augmented Generation (RAG) requires a layered caching strategy rather than simple result caching, details a four‑level cache architecture—including embedding, search, answer, and pipeline caches—provides practical key‑generation and TTL guidelines, and outlines dirty‑data defenses to keep caches consistent and performant.

AI engineeringCachingLLM
0 likes · 10 min read
How to Build a Multi‑Layer Cache for Dynamic RAG Systems
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Nov 20, 2025 · Artificial Intelligence

How to Build a Quantifiable Data Quality Framework for Dynamic Incremental RAG

This article explains why static RAG metrics don’t apply to dynamic pipelines, introduces five essential dimensions—Parseability, Deduplication, Relevance, Chunk Quality, and Freshness—and shows how to combine them into a weighted score that enables monitoring, alerts, and continuous improvement of dynamic RAG systems.

Dynamic RAGMetricsRetrieval-Augmented Generation
0 likes · 10 min read
How to Build a Quantifiable Data Quality Framework for Dynamic Incremental RAG