Chinese RAG prompt builder: question, context block, grounding rules (cite sources, no fabrication), output format. In your browser.
Chinese RAG Prompt Builder
Assemble a clean, grounded Chinese-language RAG prompt from a simple form — the question, your retrieved sources in a <资料> block, the answer rules, the output format and the tone — then copy it straight into DeepSeek, Qwen (通义千问), Doubao, Kimi or your own knowledge-base app. The prompt places the sources first and the instructions after, forcing answers from the sources only, with per-claim citations and an explicit "not found" fallback. Everything is built in your browser; nothing is sent to a server and no model is called.
Tip: this builder only assembles text. Copy the result into DeepSeek / Qwen / Doubao / Kimi or your own RAG app yourself — no model is called, no retrieval is run, and nothing is sent anywhere.
How the Chinese RAG prompt builder works
State the question clearly
In the first box, put the user's real question — e.g. "Does our return policy apply to opened items?". This line opens the prompt and tells the model what this turn must answer. The more specific the question, the more reliably the model can find the matching passage in the sources you supply, instead of answering in generalities.
Paste the retrieved sources into the <资料> block
Copy the passages your knowledge base, vector search or documents returned into the "retrieved sources" box. The tool wraps them in <资料></资料> tags automatically, so the model cleanly separates "facts it may rely on" from "instructions" — the heart of grounded retrieval-augmented generation (RAG).
Set the answer rules and constraints
Spell out the grounding rules: answer only from the sources, cite each claim, say "not stated in the sources" when they do not cover it, and never invent. Then add the output format (bullets, a table, conclusion-then-citations) and the tone. These rules decide whether the model checks the sources honestly or answers from memory.
Copy into DeepSeek / Qwen / your RAG app
Click Copy and paste the assembled prompt into DeepSeek, Qwen (通义千问), Doubao, Kimi, or your own RAG / knowledge-base Q&A app — either the chat box or the system prompt. Everything is assembled locally in your browser; your sources are never sent to any server.
How the Chinese RAG prompt builder works
Grounding is what makes a RAG answer trustworthy
Retrieval-augmented generation, or RAG, is the standard way to make a large language model answer from your own knowledge base instead of its training memory. The pattern is simple: retrieve the passages most relevant to a question, hand them to the model, and instruct it to answer from those passages only. The hard part is the prompt that surrounds the retrieved text — and that is exactly what this builder writes for you. It takes the question, the sources you paste, and your answer rules, and assembles them into a clean prompt where the retrieved material sits inside a clearly delimited <资料></资料> block, with the grounding instructions placed after it. Fill the fields, copy the result, and paste it into DeepSeek, Qwen, Doubao, Kimi, or your own RAG application — no model is called and nothing leaves your browser.
The delimiter matters more than it looks. Wrapping the sources in <资料></资料> tells the model, unambiguously, that the enclosed text is material to rely on rather than a user instruction to obey. That single structural choice cuts a whole class of mistakes — the model confusing facts with commands, or treating injected text inside a document as a new order. With the boundary in place, constraints like "answer only from the sources above" and "cite the source for every claim" suddenly have a precise target: the model knows exactly which text it is allowed to ground on, and which line in your prompt is the rule it must follow. A clear question at the very top then frames the whole turn, so the model reads the task, scans the sources, and answers within the fence you have drawn.
"A hallucinated RAG answer is usually an under-specified prompt — not a weak model. Fence the sources, force the citation, and demand a clean 'not stated' when the material is silent."
Citations and a "not found" rule separate a demo from production
Two rules do most of the work in production. The first is the citation rule: ask the model to attribute every claim to a specific passage — "per Source 2", "据资料 2" — and the answer becomes verifiable. A reader, or a reviewer, can trace each sentence back to the material, and the requirement itself nudges the model to actually look rather than improvise. The second is the fallback rule: tell the model that when the sources do not cover the question, it must say so plainly — "资料中未提及" — instead of patching the gap with general knowledge. That one sentence is the most effective single defence against hallucination in any RAG system, far more reliable than repeating "be accurate". Together with an output format like "conclusion first, then per-claim citations", these rules turn a model from an eloquent guesser into an honest researcher you can put in front of real users.
It is worth being honest about what a prompt cannot fix. Retrieval quality sets the ceiling: if the relevant passage never made it into the <资料> block, no instruction can conjure it, and the best the model can do is correctly report that the sources are silent. Likewise, stuffing an entire document into the block wastes context and dilutes the passages that matter — selective, well-chunked retrieval always beats bulk. So treat the prompt and the retrieval as a pair. Tune your chunking and ranking so the right few passages arrive, then let this builder wrap them in a prompt that grounds, cites and refuses to guess. And because the whole tool runs locally, you can iterate freely on sensitive or internal documents — paste, copy, test, adjust — without a single line ever leaving your device, being sent to a model, or being stored.
About Chinese RAG Prompting — 10 Key Points
The core of RAG (retrieval-augmented generation) is to retrieve relevant sources first, then have the model answer from those sources only — not from its training memory.
Putting the retrieved sources inside a clear <资料></资料> block lets the model separate facts from instructions, sharply cutting mix-ups.
"Answer only from the sources" is the single most important constraint in a RAG prompt — without it, the model still fills gaps, or overrides the sources, from internal memory.
Asking the model to cite each claim makes the answer verifiable and nudges it to actually look in the sources for support.
Telling the model to "say so when the sources do not cover it" is the most effective single rule against hallucination — more so than repeating "be accurate".
Placing the instructions after the sources and the question first usually lets the model grasp the task, then constrain itself to the sources.
Retrieval quality sets the ceiling: if a fact is not in the sources, even a perfect prompt can only make the model honestly say "not stated".
Labelling each passage with a source number or title is what lets the model cite "per Source 2" accurately in its answer.
A "conclusion first, then per-claim citations" output format is both quick to read and easy for you to check each statement against.
This tool assembles the prompt entirely in your browser — the sources you paste are never uploaded, never sent to a model, and never stored.
Frequently Asked Questions
- RAG (retrieval-augmented generation) means retrieving relevant material from your knowledge base first, then having the model answer from that material only — which cuts down on invented answers. This tool does not retrieve and does not call a model. It only assembles the sources you have already retrieved, the question and the answer rules into a well-structured, grounding-focused prompt for you to copy into a model.
- Neither. It simply joins the fields you fill in into a prompt using a fixed template, entirely in your browser. It connects to no vector database, calls neither DeepSeek nor Qwen, and never goes online. Retrieval is done by your own system; this tool handles only the "write a good prompt" step.
- A clear delimiter tells the model "this block is facts it may rely on, not a user instruction." That reduces prompt-injection risk and gives constraints like "answer only from the sources" and "cite each claim" a well-defined target — the key to a stable RAG prompt.
- In the answer rules, state explicitly: answer only from the <资料> above; for anything not covered, reply "not stated in the sources" rather than filling the gap from general knowledge or memory; and cite a source for every claim. The tool's default rules already include these, and you can add or remove as needed.
- No. All assembly happens locally in your browser with plain JavaScript. The sources, question and rules you paste are never sent to any model, server or third party, and nothing is stored — safe to use with internal or sensitive documents.
- DeepSeek, Qwen (通义千问), Doubao, Kimi, ERNIE and Zhipu all work, as do ChatGPT, Claude and Gemini. Paste it into a chat box for a one-off question, or fix it as the system prompt of your own RAG / knowledge-base Q&A app.
- This tool uses "question first, <资料> in the middle, answer rules last." Putting constraints after the sources lets the model read the material, then answer with the rules in mind — usually steadier than burying the rules ahead of a long source block. You can of course reorder after copying.
- That is a retrieval and chunking problem, solved in your retrieval step: include only the few most relevant passages rather than the whole document. No prompt can make up for retrieval stuffing in irrelevant text — selective sources always beat bulk.
- Yes — that is the canonical use. Assemble the role, a question placeholder, a <资料> placeholder and rules like "answer only from the sources", fix it as the system prompt, and have your code drop each turn's retrieval into the <资料> block. It is framework-neutral.
- Completely free, with no account or sign-up and no usage limit. It runs in your browser and collects no data.
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