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@兔老板工作室
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PDF of top interview Q&A
Study-abroad: OPT / timelines / résumé checklist
Every course tech stack lands in a real, clickable project — Agent eval, LLM eval, reinforcement learning, RAG… here’s your answer to the interviewer’s “what have you actually built?”
Tool-call trajectory evaluation for Agents plus automatic failure attribution — Big-Tech Agent-eval methodology as a runnable platform.
Try it online →Hallucination rate, RAG faithfulness, multi-sample LLM-as-Judge and paired significance tests — the full eval pipeline in one click.
Try it online →A memory Agent trained to decide when to retrieve and when to hold back — the full SFT → multi-step TRACE RL flow.
Try it online →AI-driven English proficiency diagnosis with an instant report — a full LLM application build end to end.
Try it online →AI script generation and novel-to-drama platform — a content pipeline from text to storyboard.
Try it online →A-share strategy research and experimentation platform with visualized strategy signals.
Try it online →AI career guidance for big-tech engineers — a decision-making assistant for crossroads moments.
Try it online →Content management for selling courses on Xiaohongshu — courses, copy and assets in one place.
Try it online →Xiaohongshu copywriting helper for farm sellers — ready-to-post copy in one click.
Try it online →Upload a pet photo — GLM-4V 6-dimension health score, emotion analysis and a personality card, instantly shareable.
Try it online →One sentence becomes a full AI drama — story, frames, characters, video, voice-over and final cut, fully automated.
Try it online →Interview quiz panel across Linux, networking, containers, K8s, CI/CD, monitoring, middleware, architecture, AI-GPU and IaC — practice by Big-Tech focus area.
Try it online →Interview quiz panel for the Qwen-Image-Edit image-editing model — 106 knowledge points across 21 tracks, from architecture to deployment, competitor comparisons and a reading of the base-model tech report.
Try it online →No sign-up needed: high-frequency interview points, RAG / Agent system-design essentials, and Big-Tech talent-program intel. Always-free long reads, updated continuously.
High-frequency Q&A across the full stack — Transformer internals, alignment, RAG, Agents, KV-Cache and quantization deployment — to spot your interview blind spots.
Read →Chunking, hybrid search, two-stage recall + rerank, multi-hop retrieval, hallucination & grounding, plus evaluation and monitoring approaches and engineering pitfalls.
Read →What makes an Agent different from chat, Function Calling, tools & memory, multi-agent guardrails, and how to approach Agent system-design questions.
Read →JD TGT / Alibaba Star / ByteDance Top Seed·Jindouyun / Tencent Qingyun / Huawei Genius / Meituan Beidou / Baidu AIDU — who can apply, what it takes, and the pay.
Read →US OPT timelines & visa pitfalls, autumn-recruiting eligibility for returnees, US vs. China résumé differences, and how interviews differ — everything in one guide.
Read →These guides cover the questions — but interviewers dig into your projects.
Want me to look at your résumé and tell you what to change? DM me on Xiaohongshu. Want the structured version instead? See the courses →
Songshu AI keeps sharing AI career-switch guides on Xiaohongshu — covering product → AI and data analysis → AI transitions, plus real mock interviews and full offer-process coaching.
A typical resume has three parts: education, projects, and papers. Songshu AI shows you how to present project experience. On projects, interviewers care about three things: what the project did, what you did, and what you achieved — on the resume that is the project context, your role, and the results.
Read →A course built from the latest Big-Tech hiring analysis — the full chain from pretraining to deployment: pretraining architectures, supervised fine-tuning (SFT), RL alignment (DPO/GRPO), chain-of-thought reasoning, knowledge distillation, and efficient LoRA fine-tuning.
Read →Follow @兔老板工作室 on Xiaohongshu for ongoing explainers and interview prep on LLMs, AI Agents, RAG and inference optimization.
Follow on XHS →Many LLM courses exist. The difference from 'note-compilation' courses is clear in this one table.
| Aspect | Free notes / rote | Other paid courses | Songshu AI |
|---|---|---|---|
| Content source | Patched-together notes | Compiled by instructors | Real interviews, from the hiring side |
| Q&A explanations | Answers only | Answers + some explain | Interviewer view: 'why ask' + scoring points |
| Resume help | ✗ | ✗ | 2× 1-on-1 review |
| Mock interview | ✗ | ✗ | Real 1-on-1 mock |
| Updates | Whenever | Semi-annual | Monthly, on 2026 trends |
| Instructor | Students / self-taught | Working engineers | Senior algorithm engineer · sits on real hiring loops |
A front-line senior algorithm engineer who sits on real hiring loops. Your teacher is the person on the other side of the interview table.
Lead editor of "图深度学习从理论到实践" and lead translator of "快速部署大模型：LLM 策略与实践" (both Tsinghua University Press), writer behind the "兔老板工作室" official account, and a long-time technical interviewer for hiring at big-tech and startups.
Long experience on the hiring side taught him exactly what lands an offer and what loses points. Those real-world signals are systematically distilled into the courses.
Mix freely by your goal — drill one topic or study systematically.
Transformer / Attention / pretraining / major open-source families. Cover 80% of frequent basics in 1-2 weeks.
RAG / Agent / Prompt engineering — pick 1-2 by target role. Ship an industry-grade demo in 2-3 weeks.
Fine-tuning / inference optimization / distributed training. Core for algorithm roles at top companies.
Resume + Q&A drills + mock interviews — turn "knowing" into "explaining well" and land the offer.
The few things people ask before signing up — answered upfront.
Structured courses covering the five core tracks — LLM fundamentals, RAG, Agents, fine-tuning and inference optimization — plus 200+ worked interview questions, two 1-on-1 résumé reviews and one full mock interview, all explained from the interviewer's side.
Yes. Follow 「兔老板工作室」 on Xiaohongshu and DM the word “资料” — you get the curated interview PDF free, permanently, with no strings and no phone number required.
A CAS PhD and senior algorithm engineer at a big-tech company; lead editor of “图深度学习从理论到实践” and lead translator of “快速部署大模型：LLM 策略与实践”, both from Tsinghua University Press. He sits on real hiring loops and writes the 「兔老板工作室」 official account.
Job seekers preparing systematically for LLM / AI interviews, and developers who want real projects on their résumé while building an LLM application from scratch.
The five tracks are RAG, Agent development, LLM fine-tuning & alignment, Transformer & inference optimization, and the résumé + mock-interview 1-on-1. With no background, start from RAG or Agent; targeting an algorithm role, focus on fine-tuning and inference optimization. The full-stack course covers all of it if you want one systematic run at an offer.
Typically: LLM fundamentals and Transformer internals, RAG, Agent applications, fine-tuning and alignment, inference optimization and deployment — plus deep dives into your own projects and coding ability. The courses are split along exactly these lines, with 200+ worked questions to cover them systematically.
Several: AI Infra learning material — graded practice with instant explanations across Linux, networking, containers, Kubernetes, CI/CD, monitoring, middleware, architecture, AI-GPU and IaC; Pet Health AI — upload a photo and get a health and mood report; and an AI short-drama studio that turns one sentence into a full episode. Agent- and LLM-evaluation platforms are also open to try.
Every course includes real projects and a full trial; try before you decide. DM 兔老板工作室 on Xiaohongshu to consult or enroll.
Materials, question banks and long reads are all free; for courses, DM us on Xiaohongshu to consult and try a trial before buying — no paywall and no sign-up here.
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