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.
The article explains Anthropic's official 5K‑token limit for SKILL.md files, breaks down the three‑level loading architecture, demonstrates progressive disclosure with concrete token calculations, and provides a step‑by‑step refactoring guide that reduces token usage while improving skill accuracy.
The article walks through a JD interview scenario where a candidate explains how a tool‑calling accuracy metric rose from 71% to 94% by detailing the full SFT data‑engineering pipeline, teacher‑model trajectory generation, quality validation, evaluation methodology, and interview‑ready talking points.
The article explains that Claude Code loads persisted memory once at REPL startup via _build_system(), inserts it as the 10th segment of system_prompt, enforces a 200‑line limit on MEMORY.md, deliberately avoids side‑effects in get_memory_dir(), and only refreshes the prompt with the /model command.
The article examines a common interview scenario where candidates are asked about LoRA rank selection, outlines two typical mistakes—guessing or staying silent—and presents a three‑step strategy of honest boundary setting, logical derivation, and asking a focused question, illustrating the approach with concrete LoRA calculations and a vLLM case study.
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.
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.
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.
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.
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.
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.
