Literature reviews are the bedrock of scientific research, but sifting through mountains of papers to synthesize existing knowledge is a Herculean task. Could large language models (LLMs) be the answer? New research explores whether AI can help automate this crucial step in the scientific process. Researchers investigated the potential of LLMs to generate literature reviews based solely on a paper's abstract, acting as a concise summary of the research. They tackled this by breaking the task down into two key components: finding relevant papers and then weaving them together into a coherent review. To find related papers, they developed a two-pronged retrieval strategy. First, an LLM extracted keywords from the abstract, feeding these keywords into search engines like Google Scholar and Semantic Scholar. Second, they experimented with embedding-based search using SPECTER2, comparing it with the keyword approach. Interestingly, combining keyword and embedding-based search significantly boosted both precision and recall—by 10% and 30% respectively. For generating the review itself, the team introduced a novel 'planning' stage. Before writing, the LLM either generated or was given a structured plan outlining which papers to cite and when. This plan acted like a roadmap, guiding the LLM’s writing. Results showed this significantly reduced 'hallucinations'—instances where the LLM fabricated information or cited non-existent papers. Human evaluations further confirmed the planning strategy’s effectiveness. Reviewers found plan-based summaries more accurate and preferred them over unstructured summaries. This research indicates LLMs have real potential in streamlining literature reviews, particularly when the task is decomposed into smaller, more manageable components. The planning approach offers a promising path to minimizing AI hallucinations, a critical step towards building reliable AI research assistants. While challenges remain in retrieving *all* relevant papers and completely eliminating hallucinations, this study paints an exciting picture of how AI can revolutionize the scientific writing process. Further research could focus on refining search strategies, improving planning algorithms, and investigating the ethical implications of widespread LLM adoption in academic research.