Building a code review Agent is tough. Actually running code requires sandboxing each repository, which is too costly computationally. Relying solely on LLMs to guess often leads to nonsense results.

Meta researchers devised a middle ground called 'semi-formal reasoning.' Essentially, it's a structured prompting technique that forces the LLM to fill out a 'logic certificate,' listing premises, tracking specific execution paths, and deriving conclusions. This drags the AI from 'intuition mode' into 'detective mode.'

Take a real-world example from Django’s repository. Two patches aimed to fix a year-formatting bug. A standard LLM sees `format()` and, out of habit, assumes it’s Python’s built-in function, incorrectly declaring both patches identical. Using Meta’s method, the Agent must follow the code logic and discovers one `format()` is overridden by a library function. It proves: one patch works, the other crashes. On Claude Opus-4.5, this technique boosted accuracy from 78% to 88%.

Of course, there’s a cost. Reasoning steps increase by 2.8x, taking longer and consuming more tokens. Also, if the model is already strong at a task, this template might not help. Sometimes, it even leads to 'overconfidence' due to overly long evidence chains, delivering wrong answers with conviction.

While some claim prompt engineering is dead, Meta’s research clearly disagrees. No model fine-tuning or complex environment setup is needed—just a structured questioning approach for significant gains. For projects wanting high-precision code analysis without burning cash on sandboxes, this is a practical solution.

Reference: [Semi-Formal Reasoning](https://arxiv.org/abs/2603.01896)