Prompt Engineering with Qwen 3.5-Plus: How to Automate Routine Work
The author presents a practical prompt-engineering scheme for routine work, using Qwen 3.5-Plus as the example. The idea is simple: first describe the goal and context, then break the task into stages, and only then assemble the final prompt with a role, communication rules and a response format. This approach yields not an "average" text but a controllable result, especially when the output has to fit the same template every time.
AI-processed from Habr AI; edited by Hamidun News
Prompt engineering with Qwen 3.5-Plus shows how to turn a neural network from a generator of "average" answers into a tool for regular routine work. The example walks through the full cycle: from clarifying the task to a finished, working prompt.
Why does a neural network give average answers?
A neural network does not understand a task the way a human does. It picks the most probable answer within a huge semantic space. If the request is vague, the model almost inevitably returns a safe, well-written but not very useful result.
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