Gene Ontology (GO) provides a robust framework for systematically describing the functional attributes of genes and proteins. However, the rapid expansion of scientific literature poses a significant challenge for manual GO curation, often resulting in data gaps and inconsistencies in functional assignments. To address this issue, we present GOFinder AI, a computational platform that automates the assignment of GO terms to genes by leveraging fine-tuned Large Language Models (LLMs). For any given biological entity (such as gene name), GOFinder AI retrieves relevant information from the latest peer-reviewed research and accurately maps it to GO terms, thereby maintaining a current and comprehensive linkage between entities and GO terms without relying solely on manual curation. GOFinder AI operates through a two-stage pipeline. First, a targeted query mechanism retrieves relevant publications (e.g., from NCBI-PubMed) for any given gene of interest, and the LLM then processes the text to extract functional evidence. In the second stage, these functional excerpts get subsequently mapped to GO annotations using the fine-tuned LLM model, with reasoning provided for each assignment. This mapping step independently runs three times, with a voting-based approach to obtain consensus annotations. To ensure the reliability of these automated annotations, we fine-tuned multiple LLMs, including LLaMA 3.1 and DeepSeek, using a large dataset of validated, manually curated GO annotations. The goal was to maximize the tool’s domain-specific accuracy and minimize the generation of erroneous (“hallucinated”) term annotations. In addition, we integrated Retrieval-Augmented Generation (RAG) into the pipeline, enabling the model to dynamically consult the GO database and incorporate the most contextually relevant terms. Our evaluation, based on model cross-comparisons, demonstrated that combining fine-tuning, parallel annotation runs, and RAG substantially improves the precision of annotations. When tested against a benchmark of over 100 manually curated GO annotations, the fine-tuned LLaMA 3.1-8B model-based system achieved higher predictive accuracy than both ChatGPT and its own zero-shot counterpart. To enhance interpretability, GOFinder AI incorporates an explainable AI framework that traces the full decision-making process behind each GO assignment, from retrieving relevant literature to selecting the final annotation. This framework highlights the specific text supporting each assignment and enables users to approve, reject, or refine annotations, creating a feedback loop that enhances model accuracy over time through reinforcement learning. Beyond gene function annotations, our results suggest that GOFinder AI can successfully assign GO terms to other biological entities, such as metabolites, broadening its applicability for systems biology and bioinformatics research. Overall, GOFinder AI represents a scalable, accurate, and transparent approach to keeping GO annotations continuously updated in the face of rapidly evolving scientific knowledge.