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Imagine saving 60% or more on your LLM API spending without sacrificing accuracy. It may sound too good to be true, but now it’s possible.
Large Language Models (LLMs) have become a crucial part of modern businesses, enabling automation, enhancing customer experiences, and driving innovation. However, hosting and managing LLMs can be a daunting task due to their high costs and complex infrastructure requirements.
One way to overcome these challenges is through prompt engineering, utilizing techniques like retrieval-augmented generation (RAG) to leverage external LLM providers such as OpenAI, Cohere, or Google. While this approach can help reduce costs, scaling LLM adoption to new use cases can lead to unforeseen expenses.
Recent research has introduced the concept of LLM Cascades as a cost-effective solution. By utilizing a combination of weaker and stronger LLM models and implementing innovative prompting techniques, organizations can achieve significant cost savings without compromising on performance.
The ‘Mixture of Thought’ (MoT) reasoning approach, which leverages two LLM models (GPT 3.5 Turbo and GPT 4) and new prompting techniques like Chain of Thought (CoT) and Program of Thought (PoT), offers a promising strategy to optimize LLM usage and reduce costs.
By incorporating MoT variants like voting and verification, organizations can achieve comparable performance to the latest LLM models at a fraction of the cost. This breakthrough not only provides substantial cost savings but also ensures reliable and accurate results.
Overall, LLM Cascades with Mixture of Thought present a practical and efficient solution for organizations looking to balance the benefits of LLM technology with the need to manage costs effectively.
Contributed by Domino Staff Software Engineer Subir Mansukhani.
