Instead of manually refining a complex prompt, create a process where an AI agent evaluates its own output. By providing a framework for self-critique, including quantitative scores and qualitative reasoning, the AI can iteratively enhance its own system instructions and achieve a much stronger result.

As an immediate defense, researchers developed an automatic benchmarking tool rather than attempting to retrain models. It systematically generates inputs with misaligned syntax and semantics to measure a model's reliance on these shortcuts, allowing developers to quantify and mitigate this risk before deployment.

Prompting a different LLM model to review code generated by the first one provides a powerful, non-defensive critique. This "second opinion" can rapidly identify architectural issues, bugs, and alternative approaches without the human ego involved in traditional code reviews.

To maximize an AI agent's effectiveness, establish foundational software engineering practices like typed languages, linters, and tests. These tools provide the necessary context and feedback loops for the AI to identify, understand, and correct its own mistakes, making it more resilient.

The long-held rule by Joel Spolsky to "never rewrite your code" no longer applies in the AI era. For an increasing number of scenarios, it is more efficient to have an LLM regenerate an entire system, like a unit test suite, from scratch than to attempt to incrementally fix or refactor it.

While AI coding assistants appear to boost output, they introduce a "rework tax." A Stanford study found AI-generated code leads to significant downstream refactoring. A team might ship 40% more code, but if half of that increase is just fixing last week's AI-generated "slop," the real productivity gain is much lower than headlines suggest.

Instead of fighting for perfect code upfront, accept that AI assistants can generate verbose code. Build a dedicated "refactoring" phase into your process, using AI with specific rules to clean up and restructure the initial output. This allows you to actively manage technical debt created by AI-powered speed.

Treat code reviews like a system to be automated. Tally every piece of feedback you give in a spreadsheet. Once a specific comment appears a few times, write a custom lint rule to automate that check for everyone. This scales your impact and frees you up for higher-level feedback.

To improve LLM reasoning, researchers feed them data that inherently contains structured logic. Training on computer code was an early breakthrough, as it teaches patterns of reasoning far beyond coding itself. Textbooks are another key source for building smaller, effective models.