Five Storage Rules for GPT Summarization Workflow — Combining Batch Processing in Game Reviews

To summarize game code reviews with GPT, store five key records: review unit, source digest, prompt/schema version, submission manifest, and terminal result. Batch processing is useful when workers can restart without guessing which changes have already been summarized. The model API is just one stage in the process, not the system of record.

For a gaming team, the practical output is a structured finding, not a paragraph that looks sensible. Use a batched workflow when reviews may wait, and make publication conditional on three checks: result belongs to the intended change, matches the declared schema, and every input has a terminal state. Storage-first is the important design question: what must remain true after a worker restart, partial result, or revised instruction?

Each review unit should have a durable record containing the stable ID, source digest, source location, and exact source text or reference. Include the schema version and allowed values, and the manifest item linking local work to remote work. The submission batch ID, request state, and timestamps make retries and polling resumable. The parsed result, validation state, and evidence separate accepted output from raw output.

A valid game-code finding should have a narrower contract than the model's vocabulary. For example, it should accept one of three risk values and require an evidence array, even if no evidence is found. Missing information should remain visible and not be guessed as a low-risk result.

To combine batch processing, start with a manifest item for each review unit and record its payload digest before submission. After result retrieval, map records by review_id, reject duplicate terminal results, and compare the returned IDs with the manifest IDs. Use separate states for different failure modes to avoid hiding too much information.