In the field of computational advertising, the integration of ads into the outputs of large language models (LLMs) presents an opportunity to support these services without compromising content integrity.

The emergence of AI-driven assistant models like ChatGPT, Gemini, and Claude has influenced how individuals interact with these technologies, increasingly using them to streamline and enhance their work. While LLMs provide a fresh way to engage with information, the most advanced models are costly to operate (Minaee et al., 2024). To date, online advertising has been one of the most successful business models of the digital economy. Ads support a wide variety of online content and services, ranging from search engines, online publishers, to video content and more. However, LLM services today predominantly follow a subscription model (OpenAI, 2024). A natural question to ask in this context is whether advertising could support LLMs to alleviate serving costs and charges to users, and what format advertising on LLMs might take. In this paper, we develop auctions that allocate online ads within the output of LLMs using the framework of retrieval augmented generation (RAG) (Lewis et al., 2020). RAG is one of the most popular techniques to integrate factual information into the output of LLMs.