A useful prompt may already be supported by information in your existing work context.
Not every task requires memorising a complex prompt first.
Relevant context that you are permitted to use can be an important part of a prompt.
Why do so many Prompt tutorials take such a roundabout approach?
The reason is that many AI tools operate with limited contextual awareness and little background information about your work.
Without relevant context, an answer may be incomplete or miss the task, so it still needs review. Prompt techniques such as examples and role instructions can help structure a request.
But what if an AI were already embedded into your workflow?
Depending on integrations, administrator settings and user permissions, group chats, documents, spreadsheets, calendars, meeting notes or emails may provide part of the working context.
For example, if you say, "Organize the data from yesterday's event," an assistant with calendar and document access could potentially confirm which event and data you mean—test this against your own setup.
If you say, "Help track the publishing progress of that tweet," an assistant with the relevant context may be able to identify which tweet and platform you mean, subject to the integrations actually connected.
If you say, "Help me check whether I’ve missed anything this week," it may be able to draw on sources like your to-do list, scheduled meetings and unread messages, where those integrations and permissions are in place.
The aim is to reduce repeated background explanation, while keeping human review in the workflow.
Using Prompts in Feishu: Five Principles to Test in Your Workflow
When the assistant is permitted to use relevant context, try a request in plain language and check the result against the task. Of course, applying a few small techniques can further enhance effectiveness 👇
Six Common Scenarios to Adapt and Test
If your accumulated context is still limited, these six templates are built around common daily situations and can be adapted, tested and reviewed before wider use.
Have you noticed?
The aforementioned five principles and six templates aren't really "Prompt techniques" at all—they’re simply the way you normally assign tasks to colleagues:
First, clarify the target or subject
Specify the location, event, and timeframe
Clearly state the required output format
The reason you need to learn Prompt writing is that, in those scenarios, the tool lacks context—forcing you to explain everything from scratch.
Within the Feishu environment, context such as your groups, documents and meeting records may already be available to the assistant, depending on your access settings.
The most efficient prompt is often the one you never had to write yourself. Where permissions allow, the content you generate daily in Feishu may serve as useful input for AI—confirm what your deployment actually exposes.
For more great articles from our archive, click the links below
What this means for Hong Kong decision-makers
For a CIO or IT lead in Hong Kong evaluating collaboration platforms, the prompt-versus-context question is really a procurement question: how much of your team's working knowledge—group chats, documents, meeting records—does the AI assistant actually see, and under what access controls? Before committing, ask vendors to demonstrate the assistant with your own sample documents rather than canned demos, and confirm how permissions, retention and data residency are handled for your organisation.
For HR and operations leaders, the practical test is simpler: pick one recurring workflow, such as weekly reporting or onboarding checklists, and run a two-week pilot with one team. Measure the time spent today as your own baseline, then compare after the trial. If you are also weighing broader office automation options, our guide to OA and online office tools for Hong Kong and Macau teams covers the wider selection landscape.