## Sources

1. [AI Is Writing Our Code Faster Than We Can Verify It](https://www.oreilly.com/radar/ai-is-writing-our-code-faster-than-we-can-verify-it/)

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### **AI Is Writing Our Code Faster Than We Can Verify It** by Andrew Stellman

**Main Arguments**
*   There is a growing "trust gap" in AI-driven development because AI can generate code much faster than human developers are able to verify it [1]. 
*   Experienced engineers currently face a frustrating false choice: either fully surrender their cognitive process to the AI and trust it blindly, or manually review every single line of AI-generated code [2-4].
*   Because of this dilemma, many senior developers restrict their use of AI strictly to low-risk tasks, such as writing unit tests or conducting initial code reviews, avoiding AI for core application logic [2].
*   The solution to this verification gap lies in "quality engineering," a discipline developed during the 1960s "software crisis" to ensure systems actually fulfill their intended purpose [5-7].
*   While quality engineering was largely abandoned by the broader software industry because it was perceived as too expensive and required dedicated specialists, AI has now made it cheap enough to reintegrate into everyday development [8-10].

**Key Takeaways**
*   To address the AI verification problem, the author developed the "Quality Playbook," an open-source skill that teaches AI coding agents (like GitHub Copilot, Cursor, and Claude Code) how to perform quality engineering tasks [10-12].
*   Modern development practices like Test-Driven Development (TDD) and Behavior-Driven Development (BDD) are useful but limited, as they only verify that the current code functions, rather than checking if the whole system meets its original intent [9, 13].
*   The Quality Playbook works by inferring a project's intent from existing artifacts—such as chat logs, README files, schemas, and defensive code—to build a comprehensive "quality infrastructure" [14, 15].
*   Using AI to verify AI is highly effective because verification involves structured, specification-driven work, which AI models excel at when provided with a clear definition of what "correct" means [16, 17].

**Important Details**
*   The author highlights the danger of blindly trusting AI by sharing an anecdote where Google's Gemini AI hallucinated a "shocking" statistic—falsely combining two unrelated surveys to claim developer trust in AI dropped from over 70% to 33% [18-20].
*   The foundational ideas of quality engineering come from W. Edwards Deming, Joseph Juran, and Philip Crosby, who taught that quality must be built into the process and defined as "fitness for use" [21].
*   The Quality Playbook generates ten specific deliverables for a codebase, taking roughly 10-15 minutes to run on a typical project [22, 23]. 
*   Key deliverables generated by the playbook include testable requirements (`REQUIREMENTS.md`), an exploration document to prevent generic AI hallucinations (`EXPLORATION.md`), and a quality constitution (`QUALITY.md`) [22].
*   The playbook implements a "Council of Three" multi-model audit, where three independent AI models review the codebase against the project specifications using confidence weighting rather than a simple majority vote [22].
*   It also generates an `AGENTS.md` bootstrap file, ensuring that future AI coding sessions automatically inherit the project's quality standards instead of starting from scratch every time [22].
*   The Quality Playbook is currently available in the `awesome-copilot` community repository and a pull request has been opened to add it to Anthropic's Claude Code skills repository [10].