  This paper explores opportunities to utilize Large Language Models (LLMs) to
make network configuration human-friendly, simplifying the configuration of
network devices and minimizing errors. We examine the effectiveness of these
models in translating high-level policies and requirements (i.e., specified in
natural language) into low-level network APIs, which requires understanding the
hardware and protocols. More specifically, we propose NETBUDDY for generating
network configurations from scratch and modifying them at runtime. NETBUDDY
splits the generation of network configurations into fine-grained steps and
relies on self-healing code-generation approaches to better take advantage of
the full potential of LLMs. We first thoroughly examine the challenges of using
these models to produce a fully functional & correct configuration, and then
evaluate the feasibility of realizing NETBUDDY by building a proof-of-concept
solution using GPT-4 to translate a set of high-level requirements into P4 and
BGP configurations and run them using the Kathar\'a network emulator.
