Boosting Coding Efficiency with AI: Our Prompt‑Driven Framework Achieves 23% Faster Delivery

Introduction

AI applications have exploded worldwide, spawning new unicorns and driving rapid valuation growth. Over the past year our team has been researching how AI can accelerate actual coding tasks. By the end of September, AI‑generated code adoption rose from 9.6% in early April to 89.2%, and demand‑delivery efficiency increased by 23.6%.

Practice Exploration

We identified four key challenges for AI‑assisted coding:

Ensuring code quality.

Deciding where generated code should reside in the project structure.

Verifying that AI truly improves efficiency.

Creating a reusable solution that can be adopted by the whole team.

Our investigation showed that most of our projects follow a three‑layer architecture (data layer → business‑logic layer → application layer). This insight led us to design an AI‑plus‑Prompt‑plus‑technical‑template‑plus‑project‑structure solution, similar to a contextual engineering approach.

AI‑Driven Coding Framework

We refined the coding workflow as follows: analyze requirements → design a technical solution → write interface specifications → implement modules from bottom to top (data → business logic → application). To guarantee high‑quality AI output we tackled two aspects:

1. Code Quality

AI inherently produces probabilistic (hallucinated) results, so we must improve both input quality and output constraints. We created a technical‑solution template that enforces clear business‑logic descriptions and rational module decomposition based on DDD principles.

1.1 Standardization

The template captures three parts of business functionality: business process + logic, business rules + data model, and maps them to the “Domain Function” and “Application Service” sections of the document.

1.2 Prompt Requirements

We distilled our coding experience into a structured Prompt that guides AI through each layer, ensuring the generated code meets our standards.

2. Architecture Compatibility

AI needs to know the project’s structure to place code correctly. We either generate the structure first or adjust an existing one based on AI feedback.

3. Coding Granularity

Given tool limitations, we limit AI tasks to class‑level granularity, aligning with the “Domain Service” and “Service Interface” columns of our template.

4. Solution Reuse

The template and Prompt are business‑agnostic, allowing any teammate to replicate the process with minimal effort.

5. Phase Summary

We now have a systematic AI + Prompt + technical‑solution + project‑structure framework.

Application Promotion

We applied the framework to a real‑world feature (low‑order‑price delivery fee). AI generated over 70% of the code, including five new tables and CRUD services for five domain services.

Goal Setting and Promotion

We chose delivery‑efficiency as the primary metric, supplementing it with AI adoption and penetration rates. To encourage usage we organized mentorship, shared best practices, and launched monthly “AI Pioneer” awards.

Beyond Coding

We also applied AI to code review (AI‑CR) and to operational Q&A via an AI chatbot, dramatically reducing manual effort.

Further Improvements

For new‑type requirements we use a “New‑Feature Prompt” plus the technical‑solution template; for modification requests we first split the PRD, then apply a “Modification Prompt”. Both approaches achieved high scores (70‑80+) in AI‑generated solutions.

AI Coding Quality Assurance

We established five defense lines:

Technical‑solution review (AI‑assisted).

Requirement‑implementation review (AI‑led).

AI code CR for robustness.

Test‑case generation and verification (AI‑assisted).

Professional QA validation (AI‑assisted).

Future Vision

We aim for a seamless, one‑click AI coding experience where a single task request triggers automatic solution design, code generation, quality checks, testing, and deployment, followed by AI‑driven operations support.