Writer's AI harness cuts token spend 38% | VentureBeat
Enterprise AI is facing an ROI paradox. While throwing more compute at the strongest foundation model works well in product experiments, the costs become unbearable when the product is deployed in production.
A new paper from researchers at Writer provides a solution that is accessible to engineering teams. The study takes a systematic look at optimizing the different components of the orchestration layer that wraps around the foundation model, aka the AI harness.
By optimizing the harness, the researchers show dramatic reductions in tokens per task, a drop in cost-per-successful-task by up to 61%, and quality that holds steady, all without changing the underlying foundation model.
Because the harness is fully under the developer's control and requires no model fine-tuning, engineering teams can apply these findings to build highly cost-efficient AI applications.
The ROI crisis of tokenmaxxing
The current state of AI engineering is plagued by "tokenmaxxing," an...
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