Training Large Language Models starts with curated, clean datasets to avoid bias. Fine-tuning adjusts weights for domain expertise, while techniques like LoRA reduce compute costs. Validation against real-world prompts ensures usability. A strong feedback loop keeps the model accurate as new data emerges.