Hello everyone,
I am a final-year medical student currently exploring how large language models could assist with structured medical research and evidence synthesis.
Over the past few months I have been developing a small experimental prototype locally that processes a clinical question through multiple reasoning stages — such as generating drafts, critique, verification, and synthesis — in order to simulate a structured research workflow rather than a single prompt response.
The project is purely exploratory and educational. My goal is to better understand how multi-stage reasoning systems might help with medical learning and literature analysis.
At the moment I am developing and testing everything independently on a personal laptop (4-core CPU, no dedicated GPU). Because of this I rely entirely on API access for the reasoning stages.
Currently the prototype uses the Gemini API through a multi-step pipeline, but since the architecture requires multiple model calls per query, I quickly run into the free-tier quota limits when trying to properly evaluate the system.
I wanted to ask the community and the Gemini team:
• Are there recommended strategies for evaluating multi-stage reasoning pipelines with the Gemini API while staying within rate limits?
• Are there architectural patterns that help reduce API usage for systems that require multiple reasoning steps?
• Are there any programs, research initiatives, or developer credits available for students working on experimental prototypes like this?
As an individual student developer with limited computing resources, any advice or guidance would be extremely helpful.
Thank you for your time, and for the work the team has done on Gemini and the developer ecosystem.
