The Model's JSON Was Almost Valid. I Made It Grade Its Own Homework for 48 Hours.
Every extraction pipeline I have ever pointed at a language model shares the same dirty secret: the JSON comes back almost valid. Almost is where the bugs live, because almost passes your eyes and then fails your schema at midnight. So I built a loop where the model grades its own homework, then let it run for 48 hours on a free server to see what breaks. The experiment The idea was simple: take…
The experiment aimed to test a language model's ability to self-correct JSON payloads that were almost valid. The JSON often came back nearly correct, but the errors were hidden until they triggered validation failures. The workflow involved feeding the model a prompt that asked it to return specific fields as JSON. After generating the JSON, a validator checked it against a predefined schema.
If the validation failed, the model received the original payload, the incorrect JSON, and the error details for a second attempt. All actions, both successful and unsuccessful, were recorded in a JSON Lines log. The process ran for 48 hours on a free server using a free model, with both passes utilizing the free model access. The setup was designed to expose the bugs that often hide in almost valid JSON responses.
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