According to DeepLearning.AI, most large language models struggle with tasks like counting specific letters in words due to tokenizer limitations and inadequate evaluation methods. In the course 'Fine-tuning and Reinforcement Learning for LLMs: Intro to Post-Training' taught by Sharon Zhou, practical techniques are demonstrated for designing evaluation metrics that identify such issues. The course also explores how post-training approaches, including supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), can guide models toward more accurate and desirable behaviors, addressing real-world application challenges for enterprise AI deployments. As reported by DeepLearning.AI, these insights empower practitioners to improve LLM performance through targeted post-training strategies.