In the interpretation of high‑throughput genomic data, the identification of candidate genes underlying differential expression or genome‑wide association study (GWAS) signals remains a major challenge. Here, we describe recent enhancements to the KnetMiner platform, which integrates knowledge mining, large language models (LLMs) and retrieval‑augmented generation (RAG) to accelerate gene discovery. KnetMiner constructs a comprehensive knowledge graph by integrating curated ontologies, structured databases and literature‑derived relationships. Upon input of a gene list or genomic loci, semantic queries extract relevant subgraphs that are transformed into context‑aware prompts for an LLM. Through RAG, the model retrieves supporting evidence from external sources - including publications and functional annotations - to produce gene summaries and prioritisation scores. We will present the platform's modular architecture and real use cases of KnetMiner assisting scientists in mining for candidate genes for complex traits in wheat and other crops.