Inspiration
Inspiration
AI is powerful, but every prompt consumes energy and compute. As AI adoption grows, inefficient prompts lead to unnecessary carbon emissions and higher costs. We wanted to build a solution that makes AI more sustainable without sacrificing performance.
What it does
Go GreenPrompt reduces AI energy usage by compressing prompts, trimming irrelevant context, and caching similar queries. It minimizes token usage before requests reach the model, lowering compute demand, cost, and carbon footprint while maintaining response quality.
How we built it
We built GreenPrompt as a lightweight middleware proxy using FastAPI. It processes incoming prompts, applies normalization and compression rules, estimates token savings, and stores responses in a cache. This allows reuse of similar queries and measurable token reduction.
Challenges we ran into
One challenge was reducing prompt size without degrading output quality. Balancing compression with clarity required careful rule design. Another challenge was accurately estimating energy savings without direct access to model-level power consumption data.
Accomplishments that we're proud of
We created a working prototype that demonstrably reduces token usage and improves efficiency. We also built measurable metrics to show token savings and potential environmental impact in real time.
What we learned
We learned that small optimizations at scale can create meaningful environmental impact. Efficient prompt engineering and intelligent caching can significantly reduce unnecessary AI compute.
What's next for Go GreenPrompt
Next, we plan to integrate real-time carbon estimation, semantic similarity caching, and support for multiple LLM providers. We aim to turn GreenPrompt into a scalable sustainability layer for AI systems worldwide.
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