Parrot: Efficient Serving of LLM-based Applications with Semantic
  Variable
Authors: name: Chaofan Lin, name: Zhenhua Han, name: Chengruidong Zhang, name: Yuqing Yang, name: Fan Yang, name: Chen Chen, name: Lili Qiu

The rise of large language models (LLMs) has enabled LLM-based applications
(a.k.a. AI agents or co-pilots), a new software paradigm that combines the
strength of LLM and conventional software. Diverse LLM applications from
different tenants could design complex workflows using multiple LLM requests to
accomplish one task. However, they have to use the over-simplified
request-level API provided by today's public LLM services, losing essential
application-level information. Public LLM services have to blindly optimize
individual LLM requests, leading to sub-optimal end-to-end performance of LLM
applications.
  This paper introduces Parrot, an LLM service system that focuses on the
end-to-end experience of LLM-based applications. Parrot proposes Semantic
Variable, a unified abstraction to expose application-level knowledge to public
LLM services. A Semantic Variable annotates an input/output variable in the
prompt of a request, and creates the data pipeline when connecting multiple LLM
requests, providing a natural way to program LLM applications. Exposing
Semantic Variables to the public LLM service allows it to perform conventional
data flow analysis to uncover the correlation across multiple LLM requests.
This correlation opens a brand-new optimization space for the end-to-end
performance of LLM-based applications. Extensive evaluations demonstrate that
Parrot can achieve up to an order-of-magnitude improvement for popular and
practical use cases of LLM applications.