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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

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Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Oct 16, 2026 · Artificial Intelligence

Debugging LLM Adapter Parameter Passing: Config vs. Generate Call Mismatch

The article reveals how LLM generation parameters like repetition_penalty and no_repeat_ngram_size can be lost in adapter layers, showing through local HF and API provider case studies that configuration dictionaries often don't fully propagate to the actual generate call, and emphasizes verifying parameters at the call boundary rather than trusting logs.

API integrationHugging FaceLLM inference
0 likes · 16 min read
Debugging LLM Adapter Parameter Passing: Config vs. Generate Call Mismatch
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Oct 15, 2026 · Artificial Intelligence

RAG Table Chunking: Why Rows Need Headers, Units, and Scoped Notes

The article explains why naive row-based table chunking for RAG loses critical context—headers, units, grouped rows, and scoped notes—and demonstrates a context-preserving approach that binds each row to its full explanatory metadata, enabling accurate retrieval without forcing models to guess meaning.

RAGchunking strategycontext preservation
0 likes · 18 min read
RAG Table Chunking: Why Rows Need Headers, Units, and Scoped Notes
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Oct 14, 2026 · Artificial Intelligence

Verifying Agent Task Completion: Three Success Layers, Acceptance Criteria, and Timeout Recovery

The article explains why a tool's success response doesn't guarantee an Agent task is done, detailing three verification layers (call, execution, task), upfront acceptance criteria, timeout recovery via state reconciliation, and evaluation metrics that separate run completion from true task acceptance.

AI AgentsAgent EvaluationError Handling
0 likes · 18 min read
Verifying Agent Task Completion: Three Success Layers, Acceptance Criteria, and Timeout Recovery
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Oct 9, 2026 · Artificial Intelligence

Why Your 93% Agent Success Rate Collapses Under Scrutiny: The Evaluation Gap Between Saying and Doing

The article details a rigorous agent evaluation framework for after-sales operations using GRPO, revealing why a 93% overall success rate masks critical failures in specific scenarios and safety guardrails, and how to structure evaluations to withstand interviewer scrutiny.

Agent EvaluationBucket AnalysisGRPO
0 likes · 18 min read
Why Your 93% Agent Success Rate Collapses Under Scrutiny: The Evaluation Gap Between Saying and Doing
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Sep 29, 2026 · Interview Experience

GRPO Interview Mastery: From Critic-Free Design to Collapse Detection & Reward Hacking

This article breaks down six high-frequency GRPO interview questions from top Chinese tech companies, covering GRPO vs PPO trade-offs, group-relative advantage calculation with concrete numbers, handling all-correct/all-wrong sample groups, KL constraint mechanics, convergence monitoring priorities, and reward hacking detection via shadow evaluation.

Advantage EstimationGRPOInterview Preparation
0 likes · 19 min read
GRPO Interview Mastery: From Critic-Free Design to Collapse Detection & Reward Hacking
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Sep 17, 2026 · Artificial Intelligence

Agent Harness: Writing a Resume That Shows Depth in LLM Agent Projects

This article breaks down the Agent Harness project—a cross-border e-commerce AI employee that validates new products in their first week—to demonstrate how to write a resume for Agent/LLM roles that highlights business value, system design, dynamic tool selection, context management, pluggable tools, stable engineering, and evaluation with concrete evidence.

LLM AgentPluggable ToolsResume Writing
0 likes · 22 min read
Agent Harness: Writing a Resume That Shows Depth in LLM Agent Projects
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Aug 27, 2026 · Artificial Intelligence

Why Agents Refund When Policy Says No: The Retrieval-Enforcement Gap

An AI agent correctly retrieves a 'no refund' policy but still executes a $50 refund, exposing the critical gap between policy retrieval and runtime enforcement; the article argues authorization must be enforced at the tool gateway with bound decisions, not just in model context, and outlines regression tests for deny paths.

AI AgentsAgent ArchitectureLLM security
0 likes · 13 min read
Why Agents Refund When Policy Says No: The Retrieval-Enforcement Gap