Can absolute beginners take it?

Yes. The only prerequisites are familiarity with basic Python syntax and knowledge of core deep-learning concepts (CNN/RNN/backpropagation); no prior LLM course is needed — complete beginners can follow. It's 28 hours total; plan to finish in 4–6 weeks.

What services come after enrollment?

Access opens immediately and everything is permanently viewable — including standard answers and reasoning paths for 200+ high-frequency questions, showcase projects you can build independently (RAG/Agent/fine-tuning etc.), an AI-direction résumé personally reviewed by the mentor, and one full mock-interview experience with detailed feedback. 1v1 slots are flexibly bookable.

LLM ApplicationsAdvancedUpdated 2026-06-01

LLM Interview Breakthrough · Full-Stack Program

Q: What does the Full-Stack Program cover?

It systematically covers six modules — LLM fundamentals, RAG, Agents, fine-tuning, inference optimization, and evaluation — with 200+ high-frequency questions explained in depth, plus two 1v1 résumé sessions and one full mock interview, improving everything from knowledge to delivery.

60 lessons 28 hours video / illustrated text

A systematic interview-prep course for LLM/AI roles, covering six modules — LLM fundamentals, RAG, Agents, fine-tuning, inference optimization, and evaluation — with 200+ high-frequency questions explained in depth, two 1v1 résumé sessions, and one full mock interview, to level up everything from knowledge to delivery.

By the end of this course you will be able to:

master the knowledge map and engineering thinking across LLM's six core modules
get standard answers and reasoning paths for 200+ high-frequency interview questions
independently build showcase projects in RAG / Agent / fine-tuning and more
get a résumé personally reviewed by the mentor and one full mock-interview experience

all six modules covered — build a complete knowledge map
200+ real questions explained one by one, on the 'why'
two 1v1 résumé sessions with personal mentor feedback
one full mock interview with detailed feedback
monthly course updates to track the latest 2026 trends
permanent Q&A group + alumni referral channel

1-on-1 Enquiry

Ask about 1-on-1 on WeChat

📱 Add me on WeChat (pyao2015) to ask about 1-on-1 enrolment

No recorded courses — 1-on-1 is the only way to enrol here

The course is organized into six modules totaling 60 lessons. Click a module to expand its detailed lessons.

01 LLM Fundamentals

12 lessons · 5.5 hours

From RNN to Transformer: the essence of the architecture evolution32 min
Self-Attention dissected in depth38 min
Positional encodings: sinusoidal, relative, RoPE, and ALiBi28 min
Pretraining paradigms: the differences among GPT, BERT, and T526 min
Mainstream open-model families: Llama / Qwen / DeepSeek / Mistral30 min
Scaling laws and emergent abilities24 min
Module quiz: 20 high-frequency fundamentals explained40 min

02 RAG Retrieval Augmentation

10 lessons · 5 hours

The full RAG architecture: retriever, knowledge base, LLM, evaluation30 min
Vector-DB selection: Milvus / Qdrant / pgvector28 min
Embedding-model selection and fine-tuning tricks32 min
Chunking strategies and document preprocessing26 min
Reranking and hybrid retrieval30 min
Query rewriting, HyDE, and multi-hop retrieval28 min
Industrial-grade RAG evaluation systems (RAGAS and more)32 min
Hands-on RAG engineering project (end-to-end demo)45 min
RAG module quiz: 30 high-frequency interview questions40 min

03 AI Agent Engineering

10 lessons · 5 hours

Agent paradigms: ReAct / Plan-and-Execute / Reflection32 min
Function calling and tool-use protocols28 min
The MCP protocol and the tool ecosystem26 min
Multi-agent collaboration: LangGraph / Autogen / CrewAI36 min
Memory systems: short-term / long-term / vector memory28 min
Agent evaluation and failure-mode analysis30 min
Industrial-grade Agent architecture case studies35 min
Agent module quiz: 25 high-frequency interview questions35 min

04 LLM Fine-tuning & Alignment

12 lessons · 6 hours

Full-parameter fine-tuning vs LoRA / QLoRA32 min
Hands-on with PEFT libraries and engineering experience30 min
Data preparation: instruction tuning / preference data28 min
SFT training pipeline (Transformers + DeepSpeed)40 min
RLHF / DPO / KTO: principles and differences36 min
Key points of reward-model design26 min
Distributed training: FSDP / DeepSpeed ZeRO32 min
Fine-tuning module quiz: 30 high-frequency interview questions40 min

05 Inference Optimization & Deployment

8 lessons · 4 hours

KV Cache principles and PagedAttention32 min
Performance comparison: vLLM / TGI / TensorRT-LLM28 min
FlashAttention principles dissected in depth30 min
Quantization techniques: GPTQ / AWQ / GGUF34 min
Speculative decoding and continuous batching26 min
Inference module quiz: 20 high-frequency interview questions30 min

06 Evaluation Systems & Résumé/Mock Interview

8 lessons · 2.5 hours of video + 1v1 service

Mainstream evaluation leaderboards and benchmarks (OpenCompass / HELM)28 min
LLM-as-Judge and human-evaluation design26 min
How to design a domain evaluation set24 min
Résumé polish essentials: technical depth vs project breadth30 min
Mock interview: technical / project / behavioral rounds35 min
🎁 Two 1v1 résumé sessions (30 min each)—
🎁 One 1v1 full mock interview (60 min + feedback)—

🎯 Who it’s for

job seekers preparing for LLM/AI interviews (fresh grads / experienced hires)
traditional algorithm engineers pivoting to AI / LLM
engineers at big tech who want to broaden their LLM knowledge laterally
graduate students / senior undergraduates starting LLM systematically
AI-startup tech leads who want to hire or evaluate candidates

🚀 What you’ll gain

the complete six-module LLM knowledge map
standard answers + reasoning paths for 200+ high-frequency questions
an AI-direction résumé personally refined by the mentor
one full mock-interview experience + detailed feedback
access to the alumni referral channel + WeChat Q&A
ongoing analysis of the latest 2026 interview trends

📋 Prerequisites

familiar with basic Python syntax
know basic deep-learning concepts (CNN / RNN / backpropagation)
ideally have run a demo with PyTorch / Transformers
no LLM prerequisites — complete beginners can follow

⏰ Pace

28 hours total; recommended pace: complete in 4–6 weeks
8–12 lessons per week, paired with weekend drilling
access opens immediately after enrollment; everything is viewable forever
1v1 résumé / mock-interview slots are flexibly bookable

The Instructor

CAS PhD · senior algorithm engineer · sits on real hiring loops · lead author of “Deep Learning on Graphs: From Theory to Practice” (Tsinghua University Press) · author behind the WeChat account 「松鼠Agent」

View the full instructor profile →

Permanent. Your access opens immediately after enrollment, and everything added through 2026 (new modules, updated interview questions, new industry-trend analysis) stays viewable forever.

Yes. The course starts from LLM fundamentals and is beginner-friendly, but basic Python / deep learning is recommended. After enrolling you'll receive a 'pre-course guide' to align your starting point.

After enrolling, a dedicated coordinator will set you up to book two résumé sessions (30 min each) plus one mock interview (60 min + detailed feedback); slots are flexible.

Yes. If you have concerns after enrolling, message me on WeChat — refunds are processed with no questions asked.

Most market courses just 'organize knowledge points,' but interviewers test 'decision ability' — why do it this way, and what's the trade-off. Our course focuses on the 'why' and real decision-making, paired with 1v1 résumé and mock interviews — something free materials and most courses can't offer.

Eight recurring categories: Transformer & Attention internals, RAG, Agents & tool calling, LoRA/QLoRA fine-tuning, inference optimization (KV Cache / vLLM / FlashAttention), evaluation & alignment, engineering (data handling, deployment, monitoring), and algorithms & code. The Full-Stack Program's 200+ in-depth explanations cover them all.

Start from the course's guided end-to-end projects: the Full-Stack Program includes four complete projects — a RAG knowledge base, Agent orchestration, model fine-tuning, and inference optimization — each letting you explain the choices and trade-offs. Interviewers value the decision-making of 'why you did it that way' far more than company names on a résumé.

Add me on WeChat (ID: pyao2015) and reply 「资料」 for a free selected real-question PDF — no quotas, no strings attached; enrolling in the Full-Stack Program adds 200+ in-depth explanations and full project templates. Free materials don't include the deep dives or 1v1 service.

📱 Add me on WeChat for the free pack