In my own experience working with AI coding assistants, I found that default models often deliver inconsistent results and unexpected behavior. For example, Claude, a popular AI, tends to produce overly complex code that ignores existing coding styles or even removes functioning code without clear reasons. This kind of output not only wastes time but also requires constant supervision, defeating the purpose of automation. To mitigate these issues, I've learned a few practical approaches. First, switching from default to more advanced models such as Opus 4.6 improves robustness. This upgrade usually means better code quality and fewer extraneous changes. Second, increasing the model’s 'effort' setting—essentially asking it to use more tokens or spend more processing resources—helps generate cleaner, more accurate code. Another effective practice is to task the AI with cleaning and auditing the project’s documentation files (e.g., CLAUDE.md) to eliminate redundant instructions that might confuse the model. Before finalizing any code commit, running additional simplification and review steps using the AI can catch issues early. Through these adjustments, I've found that AI tools not only become more reliable but also integrate better with my coding workflow, allowing me to focus on creative tasks rather than constantly fixing AI-induced errors. If you’re experiencing similar frustrations, these strategies might help you regain control and efficiency when using AI-driven coding assistance.