Summary of Alibaba’s AI Products – Alibaba Group

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Qoder

According to Qoder’s official vision, as LLM (Large Language Model) technology continues to evolve, it is rapidly transforming the paradigm of AI-assisted programming. Its applications are expanding—from simple code suggestions to end-to-end feature implementation—and the level of AI autonomy is continuously improving.

AI coding capabilities are experiencing three key stages:

Assistive Coding: Basic autocomplete and code snippet generation.
Conversational Refactoring: Interactive code modification and refactoring through dialogue.
Autonomous Programming: Delegating entire development tasks to AI agents.

This evolution transforms AI from a tool into a collaborator, capable of handling complex and long-term software engineering projects.

Summary of Alibaba’s AI Products - Alibaba Group

While social media is filled with “miracle project” stories completed from a single idea, real-world software development remains complex.

The abstract nature of software makes knowledge coordination and inheritance difficult, leading to technical debt and collaboration friction.
While AI can automate repetitive coding tasks, developers may overlook deep design and requirement clarification, resulting in poorly maintainable AI-generated code.
Human-AI collaboration is often synchronous, requiring constant back-and-forth communication. This limits AI’s efficiency and potential.

Qoder aims to build tools that maximize AI’s potential while solving practical software development challenges.

A key goal is transparency — AI should help developers understand the project’s architecture and design decisions, much like an expert deeply familiar with the codebase.

This visibility helps to:

Shorten onboarding time
Improve knowledge transfer efficiency
Provide contextual awareness for AI-generated code so it aligns with the overall project structure

When AI runs silently in the background, developers may feel a loss of control. To address this, Qoder introduces the following features:

To-Do List: Clear task breakdown
Action Flow: Real-time execution tracking

Developers can always view the AI’s plan, progress, and decisions, making the process transparent and trustworthy.

In AI coding, visibility is essential — it’s crucial for effective collaboration.

Furthermore, better context leads to better code. The key lies in Enhanced Context Engineering, which includes:

Deep Codebase Understanding: AI not only reads code—it understands its structure, dependencies, and design philosophy.
Memory: Persistent records of project history, user actions, and AI interactions to enable long-term context retention.

By enriching the input environment, Qoder provides more accurate suggestions and insights for architectural decisions — going beyond code completion to intelligent co-development.

Enhanced Context Engineering is not just a technical feature — it represents a new development mindset.

In the Agent era, developers’ roles shift from executors to requirement definers.

Chat Mode: Agent Collaboration

Guide AI through conversation.
Review, refine, and approve each change.
Ideal for short-term or iterative projects.

Quest Mode: Autonomous Delegation

Write a detailed specification (Spec).
Delegate the task to AI.
AI works asynchronously, seeking help only when necessary.
Perfect for long-term, well-defined projects.

The Spec is not just a task description — it’s a thinking and communication tool.

It aligns human and AI objectives, acts as a project compass, and becomes part of the team’s knowledge base.

Quest Mode is designed for this new paradigm — write specs, delegate tasks, and review results.

Writing specs → Reviewing → Refactoring — this is the new software development workflow.

As the number of available models grows, the question arises:

“Should users be responsible for choosing the right model?”

Qoder’s answer: “No.”

Developers need solutions, not model comparisons. They shouldn’t have to study evaluation metrics to pick the best model.

Qoder automatically routes each task to the most suitable model based on complexity and context, ensuring optimal performance without user overhead.