  In conversational AI research, there's a noticeable trend towards developing
models with a larger number of parameters, exemplified by models like ChatGPT.
While these expansive models tend to generate increasingly better chat
responses, they demand significant computational resources and memory. This
study explores a pertinent question: Can a combination of smaller models
collaboratively achieve comparable or enhanced performance relative to a
singular large model? We introduce an approach termed "blending", a
straightforward yet effective method of integrating multiple chat AIs. Our
empirical evidence suggests that when specific smaller models are
synergistically blended, they can potentially outperform or match the
capabilities of much larger counterparts. For instance, integrating just three
models of moderate size (6B/13B paramaeters) can rival or even surpass the
performance metrics of a substantially larger model like ChatGPT (175B+
paramaters). This hypothesis is rigorously tested using A/B testing
methodologies with a large user base on the Chai research platform over a span
of thirty days. The findings underscore the potential of the "blending"
strategy as a viable approach for enhancing chat AI efficacy without a
corresponding surge in computational demands.
