BestHub
Sign in
Wu Shixiong's Large Model Academy
Author

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.

111
Articles
0
Likes
241
Views
0
Comments
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
Mar 12, 2026 · Artificial Intelligence

How to Build Cross-Session Memory for RAG Chatbots: Short‑Term vs Long‑Term Strategies

This article explains the role of memory modules in Retrieval‑Augmented Generation systems, compares short‑term and long‑term memory techniques, outlines storage and retrieval methods, discusses management strategies like forgetting and deduplication, and compares LangChain and LlamaIndex implementations for practical deployment.

LLMLangChainLlamaIndex
0 likes · 11 min read
How to Build Cross-Session Memory for RAG Chatbots: Short‑Term vs Long‑Term Strategies
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Mar 11, 2026 · Artificial Intelligence

Taming Hallucinations and Multi‑Turn Failures in RAG Systems

This article breaks down the final‑mile challenges of Retrieval‑Augmented Generation—hallucinations, broken multi‑turn dialogue, prompt design, citation, and feedback loops—and provides concrete, layered solutions ranging from hard‑coded prompts and few‑shot examples to query rewriting, history management, post‑processing filters, and self‑check mechanisms.

Hallucination MitigationPrompt EngineeringRAG
0 likes · 15 min read
Taming Hallucinations and Multi‑Turn Failures in RAG Systems
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Mar 10, 2026 · Artificial Intelligence

RRF vs Weighted Sum in RAG: Boost Retrieval, Solve Timeliness & Interview Challenges

This article explains why Reciprocal Rank Fusion often outperforms weighted‑sum fusion in Retrieval‑Augmented Generation, presents a three‑layer approach to keep knowledge bases timely, discusses HyDE’s cost‑benefit trade‑offs, and offers concrete interview‑ready answers for common RAG follow‑up questions.

HyDEHybrid retrievalInterview Tips
0 likes · 13 min read
RRF vs Weighted Sum in RAG: Boost Retrieval, Solve Timeliness & Interview Challenges
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Mar 7, 2026 · Artificial Intelligence

Mastering Offline Document Parsing for RAG: From PDFs to Multimodal Knowledge Bases

This article provides a comprehensive guide to offline document parsing for Retrieval‑Augmented Generation, covering multi‑format extraction, layout analysis, OCR pitfalls, chunking strategies, hierarchical metadata tagging, and how these steps directly affect retrieval accuracy and overall RAG performance.

Document ParsingRAGmetadata
0 likes · 14 min read
Mastering Offline Document Parsing for RAG: From PDFs to Multimodal Knowledge Bases
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Feb 4, 2026 · Artificial Intelligence

Why LLM Agents Rush to Call Tools and How to Stop Them

The article explains that premature tool calls in LLM agents stem from a data‑distribution bias in fine‑tuning, and it presents practical fixes such as adding non‑tool samples, enforcing a Thought chain, and using negative sampling to teach the model when to think before acting.

AgentLLMThought Chain
0 likes · 10 min read
Why LLM Agents Rush to Call Tools and How to Stop Them
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Feb 3, 2026 · Artificial Intelligence

Why Loss Masking Is the Hidden Key to Effective LLM Fine‑Tuning

The article explains how loss masking in supervised fine‑tuning of large language models prevents the model from learning irrelevant tokens such as user inputs, system prompts, tool outputs, and padding, thereby focusing training on the assistant’s responses and improving performance and generalization.

AI trainingLLMPrompt Engineering
0 likes · 10 min read
Why Loss Masking Is the Hidden Key to Effective LLM Fine‑Tuning
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Dec 15, 2025 · Artificial Intelligence

Mastering Text2SQL: From Schema Design to Secure Multi‑Step LLM Pipelines

This article explains how Text2SQL works by teaching LLMs to understand a closed‑world database schema, constructing tightly constrained prompts, validating generated SQL, handling execution errors, and using a second LLM call to translate results into natural language, while highlighting common pitfalls and engineering best practices.

LLMSQL ValidationText2SQL
0 likes · 9 min read
Mastering Text2SQL: From Schema Design to Secure Multi‑Step LLM Pipelines
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Dec 12, 2025 · Artificial Intelligence

Why Fixing Bad Cases Beats Adding More Data in RLHF

In industrial RLHF, repairing bad cases—structural error samples—provides explicit alignment signals that improve model capability far more efficiently than simply increasing data volume, because it teaches the model how to correct mistakes rather than just exposing it to more examples.

Capability ImprovementModel AlignmentRLHF
0 likes · 9 min read
Why Fixing Bad Cases Beats Adding More Data in RLHF