Optimizing AI Workflows: What I Learned from Four Text-Analysis Trials
Complex AI workflows can become expensive surprisingly quickly. Not necessarily because the models themselves are expensive, but because small design decisions are repeated hundreds or thousands of times. A system message that is too short to benefit from input caching, a model that explains every decision when nobody needs those explanations, a task that gives the model too much freedom, or a…
When dealing with complex AI workflows, it is easy to underestimate the potential cost implications of seemingly small design choices. These decisions can compound over time, leading to unexpected large expenses once the workflow becomes larger. To better understand these dynamics, I conducted a series of text-analysis trials using a real process instead of a synthetic benchmark.
The workflow involved splitting documents into sentences, tokens, and multi-word terms, and then performing various classifications on them. Four trials were performed on the same two source texts, with different conditions applied in each trial. The trials compared token usage, caching, estimated cost, and preliminary result quality.
The experiments were conducted using AIDBDeveloper, a platform that allows for modular dashboards and provider-independent context management. The findings of this experiment provide valuable insights into optimizing AI workflows for cost efficiency.
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