Generative AI workflows are built by chaining together multiple LLM calls, each handling a sub-task such as summarization or translation. Executing these workflows involves choices about how to run each step, and those choices affect cost. A new arXiv paper introduces FlowMaker, a framework designed to make such deployments more cost-efficient.
The abstract notes that workflow execution 'requires decisions over workflow…' — the sentence is cut off, but the focus is clearly on optimising the deployment process. FlowMaker appears to target the problem of selecting appropriate execution strategies for each LLM call in a workflow.
Since the abstract is truncated, specific methods and results are not available from this source. The paper's contribution, as stated, is to address cost efficiency in generative AI workflow deployment, a practical concern as these workflows become more common.