Make AI code more usable! Trae + Volcano Engine Digital Intelligence Platform, create "evolving" intelligent applications

As AI programming tools develop rapidly today, tools such as Cursor and Trae have greatly improved development efficiency by virtue of their ability to generate code from natural language and support cross-language.

The code generated by the tool focuses on functional implementation. To create a popular product, it is not enough to have code that can implement functions. It is also necessary to track subsequent data (Track) and verify the effect, so that developers can eliminate blind spots when optimizing user experience and business decisions.

How can we make AI-generated codes truly integrated into business scenarios and achieve the leap from "usable" to "easy to use"? The deep integration of DataTester (A/B testing platform) and DataFinder (growth analysis tool) of the Volcano Engine Digital Intelligence Platform provides a scientific answer to this problem.

The pain point of AI -generated code: perfect function ≠ optimal effect

Although current mainstream AI programming tools (such as Cursor and Trae) can quickly generate application frameworks, they have two major shortcomings:

1. Lack of product analysis data : The generated App lacks tracking design and is unable to track key behaviors such as user clicks and conversion paths, resulting in no basis for optimization.
2. Insufficient experimental verification capabilities : After a function is launched, it is difficult to verify the difference in effects of different versions through A/B testing. One can only rely on subjective judgment or post-analysis, and the cost of trial and error is high.

Taking the e-commerce scenario as an example, the promotional page generated by AI may affect the conversion rate due to differences in button position and copywriting. However, without the ability to embed and experiment, developers cannot quantify which design is better, which ultimately leads to a waste of resources.

Volcano Engine DataTester + DataFinder : Complete the last link of AI tools

Combining the products of the Volcano Engine Digital Intelligence Platform (VeDI) with Trae will provide a better user experience than using a single AI programming tool; with the assistance of data products, AI programming results can be better evolved and iterated.

The two core products of the Volcano Engine Digital Intelligence Platform (VeDI) - DataTester and DataFinder , inject full-link optimization capabilities into AI-generated code through the combination of "data collection + intelligent experiments":

1. Behavioral data tracking: from "function implementation" to "data-driven"

• DataFinder provides a lightweight SDK that supports one-click integration into the code generated by Trae, automatically collects behavioral data such as user clicks, dwell time, conversion funnels, and generates visual reports.
• For example, developers can use DataFinder to analyze the user loss nodes from adding items to cart to payment on the shopping cart page generated by Trae and locate the experience bottlenecks.

2. A/B Experiment Verification: Scientific Decision-Making Replaces Empiricism

• DataTester provides three types of experimental capabilities to meet the optimization needs of multiple scenarios:

• Strategy iteration experiment : Test the effectiveness of different UI designs and algorithm strategies, such as A/B testing of recommendation algorithm models.
• Function release experiment : Combined with the Feature Flag function, the code function can be released seamlessly and in grayscale to reduce online risks.
• Growth marketing experiments : For AI-generated advertising materials and landing pages, quickly verify click-through rates and conversion rates, and optimize delivery ROI.

• For example, the application generated by Trae can compare different user registration interfaces through DataTester, collect conversion data in combination with DataFinder, and select the solution with the best conversion rate.

3. Full-link closed loop: AI is involved in the entire process from generation to optimization

• Trae generates code → DataFinder tracks points → DataTester conducts experimental verification → AI model feedback and optimization to form a complete closed loop.
• Volcano Engine DataTester supports linkage with large models. For example, it can feed back prompt optimization through experimental data to make the AI-generated code more in line with business goals.

Case practice: AI tools + Volcano Engine to unleash business growth potential

Scenario 1: Social App pop-up window optimization

• Problem : The pop-up windows generated by AI have a single style and a high user closing rate.

• plan :

• Use DataTester to create multiple pop-up design versions (such as button location, copy tone).
• Analyze the click-through rate and retention rate of each version through DataFinder.
• The experimental results show that the conversion rate of the "bottom button + interesting copy" combination increased by 32%, and it was fully launched online.

Scenario 2: E-commerce recommendation algorithm iteration

• Problem : The recommendation model generated by AI is unstable.

• plan :

• DataTester runs the old and new algorithm versions in parallel, dividing the traffic and comparing GMV indicators.
• Combined with DataFinder’s user path analysis, we can identify the preference differences of high-value groups.
• The experimental data is fed back to Trae’s AI model to optimize the subsequent code generation logic.

Future Outlook: AI Developers’ “Scientific Toolbox”

With the deep integration of Volcano Engine DataTester and DataFinder, AI programming tools are evolving from "code generators" to "business growth engines". Developers can focus on innovative design, while the platform automatically handles tedious steps such as data tracking and experimental verification. This model is not only applicable to the Internet industry, but also has great potential in digital scenarios in the fields of finance, retail, and automobiles.

Take action now :

• Visit Trae’s official website (trae.com.cn) to experience AI code generation.
• Scan the QR code to get customized solutions of DataTester and DataFinder, so that every line of code can accurately hit the business goal.

Through the dual-wheel drive of "AI generation + data intelligence", developers will truly achieve the leap from function development to value creation , and usher in a new era where efficiency and effectiveness are equally important.