How Large Language Models Empower Business Development Engineers: Data Analysis, Model Training, and Rapid Prototyping

Introduction

In traditional development processes, business development engineers focus on backend architecture, data storage, and business logic, but AI is redefining their role by becoming an accelerator for programming, data processing, and prototype development.

1. Large‑model‑assisted Data Analysis and Algorithm Training

Case Background

In a marketing scenario for Youzan, SMS outreach is frequent and costly; identifying “zombie” customers to exclude from campaigns can improve ROI.

1.1 Analysis Framework

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1.2 Using a Large Model to Identify Influencing Factors

We described the business scenario to the model, asking it to list factors that may affect user conversion. Prompt example:

##角色定义：
你是一个客户运营和业务分析专家。

##场景描述
在客户运营场景中，给客户发送各种场景的营销短信，引导客户转化。

##需求
我希望训练一个算法，能够筛选出能够转化的这部分客户。
请帮我分析影响客户转化的因子有哪些。

The model suggested dimensions such as customer demographics, consumption habits, historical behavior, assets, marketing frequency, etc.

1.3 Model Training and Effect Estimation

After selecting factors, we prepared training data and fed it to the model with a prompt like:

Lets step by step，你是一个算法工程师。
场景是：系统会推送一些包含链接的营销文案给到用户，我们需要根据一些特征值来预测用户是不是会进行点击链接

我会给你一个csv文件，其中各个列的含义如下 
- send_hour：文案发送的时间点（小时）
- member_level:用户的等级 
- msg_type:营销文案的类型 
- order_cnt:用户历史下的订单数 
- ....
- is_click: 用户是否点开了链接 1是点开了 0 是没点开

请帮我使用多种适合该场景的算法进行训练，在训练完成之后给出模型效果。
给出你的分析步骤。

The model automatically selected several suitable algorithms; performance differences were minor, so we chose logistic regression for its interpretability.

1.4 Evaluating Business Usability and Model Tuning

Initial precision was low; however, the business goal prioritized recall of positive samples over overall precision. We asked the model to improve positive‑sample recall while maintaining reasonable negative‑sample recall.

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1.5 Exporting the Model for Local Validation

After obtaining a satisfactory model, we exported its formula and validated it locally on larger datasets, confirming the expected recall improvements.

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1.6 Business Value Verification

We applied the model to real business data, estimating ROI gains and producing documentation that drove the solution into production.

Summary

The large model enabled rapid data insight and algorithm training with minimal expertise, achieving low‑cost, fast validation of business scenarios.

2. Large‑Model‑Assisted Product Prototype Design

Case Background

We aimed to build a low‑cost system for sales teams covering product knowledge learning, quizzes, exams, and scoring, using AI to generate the prototype without heavy front‑end or back‑end effort.

An Efficient Technical Stack

Python + large model. Python’s rich libraries and Streamlit enable quick page construction and deployment; the model can write engineering‑grade code on demand.

Steps

Instruct the model to create a Python project with Streamlit pages for the required features.

Iteratively describe each function (e.g., HTTP request details) and let the model generate code.

Wait for the model to produce the code.

Result

Within a week we delivered a functional prototype.

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3. Conclusions and Outlook

Data‑driven decision upgrades: Large models let business engineers bridge the gap between insight and modeling at low cost.

Agile product prototyping: “Python + Streamlit + LLM” enables rapid validation of ideas, creating business value.

As AI tools evolve, mastering them will become a core competency for business development engineers, shifting them from pure implementers to value creators.