Comparing OpenAI, Claude and Gemini for summarizing game code review findings
The article argues that the key question for game review tooling is not whether models like OpenAI, Claude and Gemini can write fluent summaries, but whether they return valid, consistently usable findings. It recommends defining a strict JSON output contract first, then testing two or three models on representative code diffs. Evaluation should weigh finding quality, output format and projected token cost, with a provider-neutral chat interface letting the contract stay fixed as models change. The author presents a TypeScript example that enforces a JSON schema with severity, title, evidence and action fields, plus an explicit empty result for reviews with no findings. Schema validation, the article notes, does not guarantee a model will catch issues such as frame-time regressions, but it makes malformed output an obvious failure.
This is an AI-generated summary. ShortSingh links to the original source for the complete article.

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