Qwen 3.5-Plus Presented as a Tool for Step-by-Step Prompts and Routine Automation

In an article about prompt engineering, the author shows how to transform a neural network from a generator of "average" answers into a tool for regular routine work. Using Qwen 3.5-Plus as an example, he breaks down the full cycle: from clarifying the task to producing a ready-to-use working prompt.

Why This Works

The main idea of the text is that a neural network does not understand a task "humanly" — it selects the most probable answer within a vast semantic space. If the request is vague, the model will almost inevitably produce a safe, well-written, but largely useless result. That is why the author suggests not asking the AI to "make it nice," but instead progressively narrowing the field of choices: defining a role, goal, constraints, and sequence of actions. The more precise the frame, the less randomness in the final answer.

Special emphasis is placed on the working context. For recurring tasks, the author recommends using projects, chat folders, or at least a pinned starter message with instructions. This is needed not for interface convenience, but for isolating different types of work. For example, to prevent a scenario for preparing educational materials from getting mixed up with coding tasks, vacation planning, or personal correspondence. This approach reduces noise and makes the model's responses more stable from session to session.

Three Layers of a Prompt

The practical framework in the article is built around three layers. The first is general context: what role the model should play and what result is needed. The second is step-by-step logic: in what order the AI should work through the task and where it must request clarifications. The third is interaction rules: exactly how to display the result, what to output in each message, and in what format to deliver the finished material.

In essence, this is an attempt to replace one large, vague request with a controlled process.

• Role: who exactly we are dealing with — an editor, a methodologist, an analyst
• Goal: what result counts as successful
• Steps: in what order the task is processed
• Format: how to show progress and the final answer

"Show only the current step.

Show progress in every message."

The author particularly emphasizes that the role should be defined specifically, not in general terms. Not just "you are an assistant," but for example "you are a methodologist and editor of educational material." This gives the model a narrower professional context and makes it easier to maintain the required style. Additionally, it is useful to prescribe output rules in advance: avoid unnecessary lists, divide text into blocks, separately output questionable fragments, do not change terminology without necessity. These small constraints often have a stronger effect on quality than trying to find the "perfect magic phrase."

Template for Routine

As a case study, the article examines a teacher who prepares lesson notes and transforms them into a presentation according to a strict template. The lesson content itself remains a human task, while the formatting, structuring, and transfer of material into a repeatable format can already be delegated to the model.

Before assembling the final prompt, the author suggests first asking the AI to pose clarifying questions: this makes it easier to capture hidden requirements that are usually forgotten on the first pass. For those prone to procrastination, this kind of dialogue also lowers the barrier to entry.

From this dialogue, a step-by-step workflow is assembled:

• Analysis and filtering of the source text
• Markup of material according to the template
• Segmentation into individual slides
• Generation of speaker notes
• Final formatting and review

Beyond the list of steps itself, execution discipline matters. The author advises asking the model to show only the current step, not skip ahead, and remind itself which step it is on. If the task is long, each stage can be worked through in separate messages or even in a new chat if the context starts to drift. That said, the article does not sell a universal recipe for all cases: on the contrary, it shows that the same framework needs to be adapted to the specific process, volume of materials, and quality criteria.

What This Means

The material clearly illustrates a shift in the very approach to working with LLMs. The value here lies not in a one-time lucky prompt, but in designing a mini-process where the neural network goes through clear stages and operates within strict rules. For business, education, and any office routine, this is an important takeaway: the best results come not from the "smartest" model on its own, but from a properly assembled context that turns it into a predictable tool.