Manual research synthesis is slow and prone to error. Learn how cumulative prompting and strict JSON validation create a robust pipeline for studies.

The script is essentially building a specialized expert out of a general-purpose model, one layer of context at a time, by providing a total environment—a sandbox of information—to ensure the highest possible chance of a valid, scientifically useful response.
https://github.com/carljuneau/scaiences/blob/master/studies%2Fllm-rob%2Fsrc%2Frun_models.py


Создано выпускниками Колумбийского университета в Сан-Франциско
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Создано выпускниками Колумбийского университета в Сан-Франциско

Nia: You know, I was just looking at this script for running risk-of-bias assessments, and it’s wild how much is going on under the hood. It’s not just sending a PDF to an AI; it’s this incredibly structured pipeline using the Gemini API to evaluate scientific studies.
Eli: Exactly! What’s really fascinating is that it doesn’t just use one prompt. It actually builds them cumulatively through four different conditions, labeled A through D. By the time you get to Condition D, the script is feeding the model the study PDF, training materials, and even a full worked example with expected JSON output.
Nia: Right, and it’s all strictly validated. If the JSON doesn't match the schema or if the model gets the study ID wrong, the script catches it and even tries a second time.
Eli: It’s a very robust way to handle automated research synthesis. Let’s break down exactly how those four prompt conditions work.