## 2 Related Work  
Various LLM-powered medical question-answering tools have emerged in recent years. These applications harness the generative capabilities of LLMs to provide increasingly sophisticated and context-aware interactions. As of the summer of 2024, the systems that are closest to DietNerd in functionality are Clinfo.ai, Consensus, OpenEvidence, and Scholar GPT. Each of these systems, as characterized in Table 1, are well-regarded and widely popular. Clinfo.ai is an open source retrieval-augmented LLM developed by a research team at Stanford that answers medical questions using articles from either PubMed or Semantic Scholar, depending on user preference [5]. In addition to developing this tool, the team also developed PubMedRS-200, a medical question-answer dataset of 200 questions built off systematic reviews that can be openly leveraged for performance evaluation [6]. Consensus is a knowledge engine capable of performing various tasks related to scientific literature including article summarization, question answering, and reference searches [7]. Built on the Semantic Scholar database, it is fine-tuned to extract key takeaways from each paper [8]. Consensus employs vector and keyword searches against abstracts and titles while also considering citation count, citation velocity, study design, and publication date to highlight and rank relevant articles in response to user queries. It is available through the OpenAI GPT Marketplace, where it is one of the most used research GPTs, and offers a premium version through a paid subscription on its own in-browser platform. Scholar GPT, available on the OpenAI GPT Marketplace, was developed by awesomegpts.ai [9]. With access to multiple research databases, Scholar GPT offers versatile capabilities such as scholarly article searches, access to papers and patents, critical reading and text summarization, data analysis, data visualization creation, and web page scraping. As of August 2024, it is the top featured research GPT. It should be noted that Scholar GPT typically requires explicit prompting to include resource hyperlinks when citing sources. OpenEvidence is a medical search platform founded by [PERSON] and maintained by a collective of researchers and medical advisors from Harvard, MIT, Columbia, and Cornell [10]. It is designed to answer health professionals' questions using research and studies. Relevant articles are displayed alongside icons indicating recent publication or top journal status. OpenEvidence was recently recognized as one of the top 100 AI companies globally [11] and is the first AI to have achieved a score exceeding 90% on the United States Medical Licensing Examination, surpassing performance of Google's Med-PaLM 2, Anthropic's Claude 2, ChatGPT, and GPT-4 [12]. Several other innovative systems have appeared as well. StatsPearls Semantic Search [13] and Elicit [14] are very comparable to Clinfo.ai performance [5]. Similarly, Scholar AI, accessible in the OpenAI GPT Marketplace or as a separately web-hosted tool, presents a well-developed approach to academic search and synthesis. Because its functionality bears resemblance to Scholar GPT and Consensus, both of which are included in our comparative evaluation, to avoid redundancy and maintain a streamlined analysis, we have chosen not to include Scholar AI in our direct comparisons, though we note its significant contributions to the field. **Enhancing LLM Performance. ** Two prominent techniques to enhance LLM performance and adaptability are in-context learning, which enables models to quickly adapt to new tasks based on provided examples, and Retrieval-Augmented Generation (RAG) systems, which combine the generative power of LLMs with external knowledge retrieval for more informed and accurate outputs. The emergence of in-context learning techniques allow for adaption to new tasks without fine-tuning [15]. Introduced in 2020 with GPT-3, this approach enables LLMs to learn from a few examples provided in the input prompt, effectively performing few-shot learning [16].