AI-Driven Software Development Transformation at Rakuten with Claude Code

Rakuten has achieved a transformative overhaul of its software development process by integrating Anthropic’s Claude Code, resulting in the following significant outcomes:

• Claude Code demonstrated autonomous programming for up to seven continuous hours in complex open-source refactoring tasks, achieving 99.9% numerical accuracy;

• New feature delivery time was reduced from an average of 24 working days to just 5 days, cutting time-to-market by 79%;

• Developer productivity increased dramatically, enabling engineers to manage multiple tasks concurrently and significantly boost output.

Case Overview, Core Concepts, and Innovation Highlights

This transformation not only elevated development efficiency but also established a pioneering model for enterprise-grade AI-driven programming.

Application Scenarios and Effectiveness Analysis

1. Team Scale and Development Environment

Rakuten operates across more than 70 business units including e-commerce, fintech, and digital content, with thousands of developers serving millions of users. Claude Code effectively addresses challenges posed by multilingual, large-scale codebases, optimizing complex enterprise-grade development environments.

2. Workflow and Task Types

Workflows were restructured around Claude Code, encompassing unit testing, API simulation, component construction, bug fixing, and automated documentation generation. New engineers were able to onboard rapidly, reducing technology transition costs.

3. Performance and Productivity Outcomes

• Development Speed: Feature delivery time dropped from 24 days to just 5, representing a breakthrough in efficiency;

• Code Accuracy: Complex technical tasks were completed with up to 99.9% numerical precision;

• Productivity Gains: Engineers managed concurrent task streams, enabling parallel development. Core tasks were prioritized by developers while Claude handled auxiliary workstreams.

4. Quality Assurance and Team Collaboration

AI-driven code review mechanisms provided real-time feedback, improving code quality. Automated test-driven development (TDD) workflows enhanced coding practices and enforced higher quality standards across the team.

Strategic Implications and AI Adoption Advancements

1. From Assistive Tool to Autonomous Producer: Claude Code has evolved from a tool requiring frequent human intervention to an autonomous “programming agent” capable of sustaining long-task executions, overcoming traditional AI attention span limitations.

2. Building AI-Native Organizational Capabilities: Even non-technical personnel can now contribute via terminal interfaces, fostering cross-functional integration and enhancing organizational “AI maturity” through new collaborative models.

3. Unleashing Innovation Potential: Rakuten has scaled AI utility from small development tasks to ambient agent-level automation, executing monorepo updates and other complex engineering tasks via multi-threaded conversational interfaces.

4. Value-Driven Deployment Strategy: Rakuten prioritizes AI tool adoption based on value delivery speed and ROI, exemplifying rational prioritization and assurance pathways in enterprise digital transformation.

The Outlook for Intelligent Evolution

By adopting Claude Code, Rakuten has not only achieved a leap in development efficiency but also validated AI’s progression from a supportive technology to a core component of process architecture. This case highlights several strategic insights:

• AI autonomy is foundational to driving both efficiency and innovation;

• Process reengineering is the key to unlocking organizational potential with AI;

• Cross-role collaboration fosters a new ecosystem, breaking down technical silos and making innovation velocity a sustainable competitive edge.

This case offers a replicable blueprint for enterprises across industries: by building AI-centric capability frameworks and embedding AI across processes, roles, and architectures, organizations can accumulate sustained performance advantages, experiential assets, and cultural transformation — ultimately elevating both organizational capability and business value in tandem.

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How I Use "AI" by Nicholas Carlini - A Deep Dive

This article, "How I Use 'AI'" by Nicholas Carlini, offers a detailed, firsthand account of how large language models (LLMs) are being used to enhance productivity in real-world scenarios. The author, a seasoned programmer and security researcher specializing in machine learning, provides a nuanced perspective on the practical utility of LLMs, showcasing their capabilities through numerous examples drawn from his personal and professional experience.

The article reveals the significance of LLM in solving practical problems and personal efficiency, which is specific, practical and accurate. It is a best practice for personal use of LLM use cases.

Central Insights and Problem Addressed:

Carlini's central argument revolves around the demonstrable usefulness of LLMs in today's world, refuting the claims of those who dismiss them as hype. He argues that LLMs are not replacing humans but instead act as powerful tools to augment human capabilities, enabling individuals to accomplish tasks they might have previously found challenging or time-consuming.

The main problem Carlini addresses is the perception of LLMs as either overhyped and destined to replace all jobs, or as useless and contributing nothing to the world. He aims to ground the conversation by showcasing the practical benefits of LLMs through concrete examples.

Carlini's Solution and Core Methodology:

Carlini's solution centers around the use of LLMs for two primary categories: "helping me learn" and "automating boring tasks."

Helping Me Learn:

• Interactive Learning: Instead of relying on static tutorials, Carlini uses LLMs to interactively learn new technologies like Docker, Flexbox, and React.
• Tailored Learning: He can ask specific questions, get customized guidance, and learn only what he needs for his immediate tasks.

Automating Boring Tasks:

• Code Generation: From creating entire web applications to writing small scripts for data processing, Carlini leverages LLMs to generate code, freeing him to focus on more interesting and challenging aspects of his work.
• Code Conversion and Simplification: He uses LLMs to convert Python code to C or Rust for performance gains and to simplify complex codebases, making them more manageable.
• Data Processing and Formatting: Carlini uses LLMs to extract and format data, convert between data formats, and automate various mundane tasks.
• Error Fixing and Debugging: He utilizes LLMs to diagnose and suggest fixes for common errors, saving time and effort.

Step-by-Step Guide for Newcomers:

1. Choose an LLM Platform: Several options are available, such as ChatGPT, Google Bard, and various open-source models.
2. Start with Simple Tasks: Practice using the LLM for basic tasks, such as generating code snippets, translating text, or summarizing information.
3. Experiment with Different Prompts: Explore various ways to phrase your requests to see how the LLM responds. Be specific and clear in your instructions.
4. Learn Interactively: Use the LLM to ask questions and get guidance on new technologies or concepts.
5. Automate Repetitive Tasks: Identify tasks in your workflow that can be automated using LLMs, such as data processing, code generation, or error fixing.
6. Iterate and Refine: Review the output generated by the LLM and make adjustments as needed. Be prepared to iterate and refine your prompts to get the desired results.

Constraints and Limitations:

• Data Dependence: LLMs are trained on massive datasets and may not have knowledge of very niche or recent information. Their knowledge is limited by the data they have been trained on.
• Hallucination: LLMs can sometimes generate incorrect or nonsensical output, often referred to as "hallucination." Users must be critical of the information generated and verify its accuracy.
• Lack of Real-World Understanding: While LLMs can process and generate text, they lack real-world experience and common sense.
• Ethical Concerns: The training data for LLMs can contain biases and potentially harmful content. Users must be aware of these limitations and use LLMs responsibly.

Summary and Conclusion:

Carlini's article underscores the transformative potential of LLMs in today's technological landscape. He argues that, while not without limitations, LLMs are valuable tools that can be used to significantly enhance productivity and make work more enjoyable by automating mundane tasks and facilitating efficient learning.

Product, Technology, and Business Applications:

The use cases presented by Carlini have broad implications across multiple domains:

• Software Development: LLMs can automate code generation, conversion, and simplification, leading to faster development cycles and reduced errors.
• Education and Learning: LLMs can provide personalized, interactive learning experiences and facilitate quicker knowledge acquisition.
• Research: LLMs can automate data analysis and processing, allowing researchers to focus on more complex and high-level tasks.
• Content Creation: LLMs can assist in writing, editing, and formatting text, making content creation more efficient.
• Customer Service: LLMs can be used to build chatbots and virtual assistants, automating customer support and improving response times.

By embracing these opportunities, businesses can leverage LLMs to streamline their operations, enhance their offerings, and gain a competitive edge in the rapidly evolving technological landscape.

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