Large Language Models (LLMs) can be made better at complex reasoning tasks through techniques like few-shot prompting and Chain of Thought (CoT) reasoning, which allow smaller models to match the performance of larger ones. These approaches help models break down and solve problems step-by-step while avoiding the increased costs and slower processing times associated with using bigger models.