Improving code suggestions from AI-powered development tools is crucial for enhancing code quality, user experience, and accessibility in software development. The current limitations of these tools, such as the tendency to provide misleading or irrelevant code snippets, highlight the need for refinement in their training methods and contextual awareness. Strategies like providing clear instructions, utilizing system messages, automated filtering of suggestions, leveraging context more effectively, exploring multiple LLMs, fine-tuning existing models, and domain-adaptive continued pre-training can significantly enhance the quality of recommendations. Collaboration between tool creators and the developer community is essential to unlocking the full potential of AI as a reliable coding partner.