AI metrics

Behind an AI Output Rate of 70%+: How the AutoNavi Team Quantifies and Optimizes AI Development Efficiency

Honghao Wang

— 4 min read
Behind an AI Output Rate of 70%+: How the AutoNavi Team Quantifies and Optimizes AI Development Efficiency

Preface

image
image

---

Alibaba Assistant's Preface

This guide explains, step-by-step, how to design a scientific and implementable set of quantitative R&D efficiency metrics in the era of AI-assisted programming.

---

1. Introduction

With AI advancing rapidly, AI-assisted programming has moved from simple code autocompletion to full-fledged AI IDEs like Cursor or Qoder, boosting efficiency dramatically.

However, while many companies claim productivity gains, few can measure them accurately. Challenges include:

  • No unified metric system
  • Data collection difficulties due to varied AI tools
image

Over months of R&D, we built an AI-unified data collection capability from scratch, defined key KPIs such as AI Code Adoption Rate, and iteratively built an AI efficiency metrics framework—closing the loop: measurement → problem identification → optimization.

---

2. Metric Definitions

Principle: Authenticity First

We only measure code actually submitted to Git, ensuring quality and compliance.

By analyzing Git commits and matching AI-generated code, we can quantify AI's impact on productivity.

---

2.1 Core Metric — AI Code Adoption Rate

Definition:

image
  • AI-adopted lines: AI-generated lines that match commits line-by-line
  • Total commit lines: All lines committed within the time window

---

2.2 Additional Metrics

Supporting metrics for deeper insights:

image
  • Code volume statistics — Tracks amount of code produced
  • Conversation metrics — e.g. prompts, acceptance rate, dialogue rounds
  • Usage duration stats — Tool stickiness measurement
  • Tab completion analysis — Share of Tab-based code in adoption rate

---

3. Metric Results

We applied metrics to the Amap (Gaode) Information Engineering and frontend teams using Cursor & Qoder.

Example: August stats showed adoption rising from 30% to >70% in just 3 months.

image

Optimization included:

  • Targeting non-ideal AI scenarios
  • Adding rules & MCP tooling for document query
  • Improving applicability & efficiency

---

Closing Insights

Building authentic, unified, actionable metrics guides both internal tooling and process optimization.

Open platforms like AiToEarn官网 apply similar data-driven loops for creative workflows — measuring, optimizing, and monetizing AI output.

---

Best practices:

  • Continuously refine usage strategies — Use metric feedback to compare tools & approaches
  • Accumulate usage models — Share methods, host competitions, leverage top performers’ practices

---

4. Solution Overview

We use a layered architecture for indicator evaluation:

image

Main modules:

  • Multi-IDE plugins (Cursor, Qoder, etc.)
  • Data collection: Tab completions, AI session logs, generated code
  • Local adaptation: IDE-specific pipelines
  • AI rule & MCP capabilities
  • Dynamic project rules
  • MCP injection for doc retrieval & code data
  • Platform components
  • Storage, task scheduling, calc engine
  • Indicator dashboards (team & personal)

---

5. Practice Process

Core capability: Unified data collection + metrics framework

---

5.1 AI Code Generation Data Collection Analysis

Three mainstream approaches:

  • Git commit signatures — Mark AI commits at commit level
  • IDE plugin tracking — Line-level tracing via blame & Diff
  • Local DB reverse-engineering — Read AI editor DB for structured session data
image

---

Our Method:

We evolved from DB reverse-engineering → MCP protocol standardized collection

Example: Using AiToEarn integration for multi-platform publishing & analytics complements these metrics, making AI productivity measurable beyond coding.

---

MCP Standardized Collection Advantages

  • Works across IDEs & CLI tools
  • Future-proof & compatible
  • Lower engineering complexity

Workflow:

  • Prompt forces MCP execution
  • MCP records before/after file edits
  • Diff analysis calculates AI-generated lines
image

Drawbacks:

  • Dependent on prompt usage quality
  • Data not invisibly collected

---

5.2 Prompt Design & Optimization

We split rules into:

  • Business rules — modular, knowledge-base driven
  • Intelligent collection rules — targeted prompt injection for clean MCP execution

Optimized business rule flow:

image
image

---

MCP Automatic Collection Policies

Core Principles
  • Only changed files recorded
  • Trigger before/after operations like `create_file`, `edit_file`, `delete_file`
Execution Flow
Pure chat:
Conversation ends → recordSession

File changes:
beforeEditFile → [op] → afterEditFile → recordSession
Mandatory Requirements
  • 100% coverage
  • Strict before/after pairing
  • Absolute paths
Violation Handling

Immediate detection, correction, re-run

---

5.2.3 Results

  • Cursor → DB reverse-engineering
  • Claude Code + Qoder → MCP triggering rules

---

5.3 Metrics Calculation

Stages:

  • Prepare data — Collect Git & AI code sets
  • Match code lines
  • Calculate metrics (V1 basic, V2 filtered, Quantile-enhanced)
image
image

---

6. Summary

Different AI tools have unique workflows, but measuring AI code generation rate lets teams:

  • Quantify adoption
  • Guide shifts from passive AI use → active AI-driven coding

We moved from manual coding to AI-assisted development in under 3 months — boosting efficiency and AI fluency.

---

Reference:

Kernel AI Coding Assistants Rules Proposal

---

Qwen-Image Tip:

Use Qwen-Image for clear multilingual text in AI-generated graphics, plus image-to-video conversion.

---

Platforms like AiToEarn官网 unify technical AI usage metrics with creative publishing analytics — bridging code-gen KPIs and content monetization pipelines.

Read Original

Open in WeChat

Read more