Most disappointing AI answers are context problems, not model problems. Here is a practical checklist for deciding what background to include — and what to leave out.
2026-09-12 · 约 4 分钟读完
作者：陈叙（CPCX 内容负责人 · 提示词与方法）
I ran the same request twice — once as “help me write a follow-up email” and once with the recipient, the history, and the tone spelled out. The first version needed four rounds of correction; the second needed one. That gap is what this article is about.
When an AI answer feels generic, the cause is usually not the model. It is that the model had to guess everything you did not say: who the output is for, what happened before, what format you expect, and what a good result looks like. People fill these gaps from shared context without noticing; a language model can only interpolate from your words.
A useful mental test: imagine handing your request to a competent new colleague who joined today, with no access to your inbox, your files, or your previous conversations. Anything that colleague would need to ask about is context you should include.
More context helps only when it is relevant. Pasting an entire project wiki into a request for a short email does not improve the result; it gives the model more places to pick up the wrong emphasis. The skill is curation: a few sentences of the right background beat several pages of the adjacent ones.
A practical rule: for every sentence of instruction, ask whether the model could have guessed it. If yes, cut it. If no, keep it.
Once the context is in place, treat the first answer as a draft to be steered. Feedback like “make the second paragraph shorter” or “drop the apologies and state the fix directly” converges much faster than restarting with a longer prompt. Each round of feedback is itself context.
This is also why conversation history matters. Inside one thread, the model remembers your earlier corrections; if you start a new chat, all of that is gone. Keep one thread per task, and start fresh when you switch tasks.
Every gap you leave will be filled with something statistically plausible — not necessarily true. This is most visible with facts: names, numbers, citations, product details. Plausible-sounding filler is exactly what AI hallucination looks like in practice.
So separate the two kinds of content in any answer: things that were in your context (safe to trust) and things the model added (worth checking). For each factual claim, ask yourself: did I give it this, or did it come up with this?
Save the briefs that work. If a request needed real background the first time, it will need it every time — that is exactly what our prompt template library encodes: the context checklist, pre-assembled.