  Large Language Models (LLMs) have emerged as powerful tools capable of
accomplishing a broad spectrum of tasks. Their abilities span numerous areas,
and one area where they have made a significant impact is in the domain of code
generation. Here, we propose using the coding abilities of LLMs to introduce
meaningful variations to code defining neural networks. Meanwhile,
Quality-Diversity (QD) algorithms are known to discover diverse and robust
solutions. By merging the code-generating abilities of LLMs with the diversity
and robustness of QD solutions, we introduce \texttt{LLMatic}, a Neural
Architecture Search (NAS) algorithm. While LLMs struggle to conduct NAS
directly through prompts, \texttt{LLMatic} uses a procedural approach,
leveraging QD for prompts and network architecture to create diverse and
high-performing networks. We test \texttt{LLMatic} on the CIFAR-10 and
NAS-bench-201 benchmarks, demonstrating that it can produce competitive
networks while evaluating just $2,000$ candidates, even without prior knowledge
of the benchmark domain or exposure to any previous top-performing models for
the benchmark. The open-sourced code is available in
\url{https://github.com/umair-nasir14/LLMatic}.
