Let the big model think, let the small model work: Splitting LLM costs in Elastic Workflows

Here's the thing: while this approach sounds efficient, it raises questions about actual cost savings and the complexity of human-in-the-loop workflows. Relying on a large model for initial classification before human approval may seem like a smart move, but it also introduces bottlenecks and delays. If you're already using LLMs like OpenAI's GPT-4 or Google's BERT directly for classification, transitioning to this model could require significant retraining and adaptation of your existing systems.

To be clear, this method could work well for teams with a clear understanding of their data quality issues and the overhead of human review. If your organization is already invested in Elastic and can effectively manage these workflows, then it might just save you some bucks in the long run. However, if your data quality is still a significant concern, then adding this layer could complicate matters instead of resolving them.

The catch here is the maturity of this solution. Being in early GA means you might encounter kinks that haven’t been fully ironed out. Expect to navigate the nuances of integrating human review into your automated process. If your team is already stretched thin managing pipelines, this could add to your technical debt, especially if the benefits aren't immediately clear.

So, who benefits? Teams that have a reliable human review process and want to optimize model costs without sacrificing quality might find value here. But if your model performance hinges on quick iterations and feedback, you could be better off sticking with a more straightforward approach for now. My recommendation? Evaluate how this workflow fits into your existing stack before diving in.