Context Slicing: How OSS Maintainers Can Get Better AI Code Reviews on Free Tiers

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AI-assisted pull request reviews often fail not because of poor models but due to context pollution, where irrelevant diffs, stale comments, and duplicate snippets overwhelm the model's attention. A technique called context slicing addresses this by feeding the AI only three focused layers: the diff and commit message, the definitions and tests touched by the change, and exact commands with expected outputs. A simple shell script can extract these layers into a compact file that fits within a free-tier prompt window, making the review loop faster and cheaper. Pairing the sliced context with a structured prompt that restricts the model to only the provided diff further reduces hallucinated or invented analysis. The approach prioritizes a smaller, well-curated context over a larger one, arguing that task specificity matters more than model size.

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