Saiyi Information's official website article on August 7, 2026 called FDE "field-driven engineering", introduced the two paths of standard-driven development and intelligent agent delivery, and disclosed the AI coding practice of the shipping notice functional module of grain and oil manufacturing enterprises. This article retains the company's original Chinese name and does not expand it into English. The customer is not named, and figures such as development speed are only self-reported by the company.
A business document name cannot be directly turned into a development requirement.
The following is an independent analysis put forward by ChinaOPC combined with document software, and is not the actual functional design of this customer. When developers hear "make a shipping order," they still don't know whether it expresses plans, issues notifications, or records actual operations. The three meanings may have similar fields but have different impacts on subsequent processes. First confirm with the business staff what work the document will do, and then decide what the page and code need to perform.
We can start with an operation by the user: what data he uses to create the document, who he wants to know after submission, and in which states it can be modified. Cancellation means withdrawing the notification or changing the original plan. These questions should be written into business sentences that can be discussed. AI can help discover omissions, but it cannot infer the entire operation process for customers just based on the name of the document.
Behind the quantity field there are units and calibers.
For example, a certain type of document may contain both planned and actual quantities. Whether the two use the same unit, whether it can be zero, whether decimals are allowed, and how to deal with exceeding the plan need to be confirmed separately. This example is a method description, not a table of fields disclosed in the report. If the specification only says "Quantity is required", although the generated code can verify null values, it may not necessarily inherit the business meaning.
The source of the default value should also be questioned. Whether a certain option is the result that the system can determine, or whether it is just a suggestion given to facilitate entry, cannot be confused. A seemingly trouble-free default state may make subsequent personnel mistakenly think that a certain pre-step has been completed. By writing these judgments in the functional description, business people will have the opportunity to correct them before implementation.
New functions must be connected to existing system conventions
Document functionality is usually not an isolated page. Where the data comes from, which systems can see it after saving, and whether the draft will be retained in case of failure are all related to the existing business. A small team can first draw the relationship between input, processing, and result users, and then check what actions the existing system can support. Just because AI generates a set of interface calling codes, it does not mean that external systems also have corresponding capabilities.
When an exception occurs, the prompt should explain the current facts. For example, users should be able to distinguish between unsuccessful notification sending and unsuccessful document saving; otherwise, users may repeatedly create orders or mistakenly believe that subsequent personnel have received them. What is discussed here is how to express business information, not rewriting message retry or system integration details for the original case.
Business specifications need to be understandable to users
Specifications can be organized from a set of specific situations: normal creation, supplementary information, change of plan, withdrawal notice, inability to obtain necessary data. Each scenario describes who is operating, what is known, what is expected to be seen, and what remains to be confirmed. Compared with a long prompt full of technical words, such a description is more convenient for business personnel to judge whether it meets work habits one by one.
After AI generates code, business personnel can view the functions along the same set of scenarios and observe whether the pages and prompts still express the original meaning. When problems are discovered, return to the missing or ambiguous business description instead of endlessly appending patch prompts. For small teams, a trial run of specific functions can help determine whether the requirements are clear enough, but it cannot be used to ensure that all enterprise software can be delivered quickly and automatically.
The Saiyi report shows a specific clue of the combination of on-site needs and AI development. This article does not use the efficiency figures to draw general conclusions, nor does it classify this large service provider or anonymous customer as OPC. What readers can learn from is to explain a familiar business action clearly, and then study which part of the implementation work AI can undertake.
Extended reading: [How to write clear results for small team projects] () and [How to correspond to requirements in quotations] () to understand how business description affects the scope of implementation. Related reading: How to write down the results of a small team project; How does the quotation correspond to demand?.