A line that stops a job seeker
On a Tuesday evening in Hangzhou, a 26-year-old job seeker I will call Xiao Lin was scrolling through BOSS Zhipin, one of China’s most popular hiring apps. She stopped at an e-commerce operations role. Among the requirements was a line that looked almost casual: “Familiar with large-model tools; able to design prompts independently.”
Her first thought was not “great, a new skill.” It was: “Do they expect me to train a model?” For readers outside China, the confusion is fair. In Chinese job ads, “大模型” literally means “large model,” but in practice it is a baggy term covering everything from using a chatbot to summarize a meeting to building a retrieval system for company documents.
That gap between the phrase and the work is what sent me through 100 Chinese job listings. The phrase is not meaningless. It is just doing too many jobs at once.
How I read 100 job descriptions
I collected 100 listings that mentioned “大模型,” “LLM,” “AIGC,” or “generative AI” on BOSS Zhipin, Liepin, and 51job/Zhaopin between late 2024 and early 2025. They came from Beijing, Shanghai, Shenzhen, Hangzhou, Guangzhou, and Chengdu, across internet, e-commerce, fintech, manufacturing software, education, healthcare IT, content, and enterprise services. The levels ran from intern and junior roles to team lead.
This is not a statistical sample. I chose public, active listings, skipped duplicates where I noticed them, and treated each ad as a language sample, not a census. Chinese job ads are also marketing documents. They are written to attract applications, filter resumes, and sometimes impress clients. That is exactly why they are useful: they show how a technical idea gets translated into ordinary workplace language.
Seven meanings behind one phrase
1. Using a chatbot to get through office work
The most common version is the least technical. A listing might say: “熟练使用DeepSeek、豆包、ChatGPT等大模型工具提升工作效率.” That means using AI to draft emails, summarize meetings, generate marketing copy, write spreadsheet formulas, or prepare customer replies. The bar is close to “can use Excel well.” You need judgment, not a PhD.
2. Prompt writing as a workflow skill
Another common line is “能设计提示词、搭建提示词库.” This sounds more mysterious than it is. In daily work, it means writing clear briefs, giving examples, setting constraints, and testing whether the output is usable. It is less like casting magic spells and more like briefing a fast intern who forgets everything unless you write it down.
3. Calling APIs
Then the ads get more concrete: “熟悉主流大模型API，能完成接口调用.” This is a real engineering gate. It means a developer can connect a model to an app, handle JSON, manage keys, deal with errors, and watch cost. Python or JavaScript basics usually come with the job.
4. RAG and fine-tuning
A mid-level application role may ask for “了解RAG、向量数据库、LoRA微调.” RAG is easiest to picture as an open-book exam: the system first finds relevant company documents, then asks the model to answer using them. Fine-tuning is closer to training by example. These are applied AI skills, not frontier research.
5. Deployment and inference
Some listings mention “模型部署、推理优化.” That is the unglamorous work of making a model run reliably and affordably: latency, GPUs, containers, monitoring, and cost per request. It matters more than demo videos.
6. Algorithm research
Only a handful of ads ask for “熟悉Transformer，有预训练或论文复现经验.” These are the hardest roles, usually for candidates with a master’s or PhD, strong open-source work, or real model-training experience. They are a small slice of the 100.
7. Product and business translation
Product and industry roles often say “理解业务场景，能将大模型能力转化为产品方案.” No coding may be required. The work is deciding where AI helps, where it fails, how to test it, and how to keep private data out of trouble. It is translation between business and engineering.
A job map: who is actually hiring
Once you separate these meanings, the listings stop looking like one wall of hype.
Product
AI product managers are asked to know the “boundaries” of large models. In practice, they write specs, test prototypes, and decide whether a feature saves time or just looks impressive.
Operations
E-commerce, content, community, and customer-service operations roles mention AI for copywriting, reply drafts, user segmentation, and knowledge bases. The daily work is a prompt library plus quality checks.
Data annotation and evaluation
“大模型数据标注/评测” roles are an entry point. People compare answers, write rubrics, and flag hallucinations. The work can be repetitive, and pay varies widely, but it teaches what good output looks like.
Development
Backend and full-stack developers build internal document Q&A, chat assistants, and workflow tools. Software fundamentals matter more than the model brand.
Algorithm
NLP and LLM engineers train, fine-tune, and evaluate models. The bar is higher, and the interviews are more technical.
Industry solutions and pre-sales
Finance, healthcare, and manufacturing roles need people who can turn a customer’s messy process into a demo and a proposal. Communication can matter more than math.
Recruiter language vs. the actual job
In the 100 ads, a few requirements looked like real gates: production API integration, RAG systems, evaluation, data security, and cost control. Prompt writing and familiarity with ChatGPT-style tools were usually nice-to-haves. “Understands large models” in a sales, admin, or marketing ad was sometimes decoration.
Why the decoration? Hiring managers use keywords to filter resumes. HR teams copy phrases from competitors. Companies want clients and investors to see them as AI-enabled. A real interview usually reveals the truth. If the ad says “large models” but the interview is all about Excel and client schedules, the phrase was packaging. If they ask you to build a small Q&A bot from a product manual or explain how you would test a customer-service prompt, the skill is real.
How ordinary young people close the gap
You do not need an expensive bootcamp to start. Build one small project with public documents: make a document Q&A tool, measure how often it is right, and note where it fails. Learn enough Python to call an API. Create a prompt library for a real workflow. Practice evaluation: correctness, hallucinations, latency, and cost.
In interviews, be ready to explain the difference between RAG and fine-tuning, how you would test an AI answer, and how you would handle sensitive company data. At work, volunteer to automate one boring task and document the before-and-after time saved. For non-coders, the valuable role is often the workflow translator: the person who knows which task is worth automating and how to check the output.
Why companies write it this way
Chinese companies talk a lot about “降本增效”—cutting costs and improving efficiency. Most of the AI roles in my sample were about applied work, not building a new frontier model. Companies want document search, customer service, marketing copy, coding assistance, and internal tools. They also face fierce competition for AI talent, so ads borrow the hottest vocabulary. Domestic model names show up often: DeepSeek, Qwen, Doubao, Wenxin, GLM. Access, cost, and data rules shape which tools a team uses.
The actual work is often less exciting than the ad. It involves cleaning documents, writing test cases, fixing integrations, and explaining to colleagues why the chatbot gave a wrong answer. That is not a failure of the technology. It is what adoption looks like.
For readers outside China: same vocabulary, different accent
Silicon Valley job ads use the same words: LLM, RAG, evals, prompt engineering. The difference is emphasis. Chinese listings often blend AI language into ordinary product, operations, and sales roles more openly. Descriptions can be shorter, and hiring often happens through chat on apps like BOSS Zhipin. You may see domestic model names instead of OpenAI alone.
In both places, there is a split between research scientists and applied AI engineers. In China, the phrase “大模型” has spread faster into non-technical job titles. That reflects a labor market looking for practical deployment, and a lot of companies trying to look ready for AI at the same time.
The phrase is a door, not a label
“LLM experience” is not one skill. It is a door that opens onto very different rooms. When you see it, ask three questions: Which tool or model? What task? What evidence? A good job ad answers them. A lazy one hides behind the buzzword.
Xiao Lin eventually rewrote her resume line. Instead of “familiar with large models,” she wrote: “Used AI tools to draft customer replies and built a prompt library for a 12-person team.” That sentence is smaller, clearer, and far more believable. The same goes for the phrase itself. Behind the hot word is a set of concrete tasks—and that is where the real hiring happens.
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