AI coding tools still suck at context — here’s how to work around it

In the evolving landscape of software development, AI coding tools, though promising, pose significant challenges due to their finite context windows, often leading to inefficiencies and increased debugging efforts. The 2025 Stack Overflow Developer Survey highlights a decline in trust and accuracy in AI tools, with only 33% of developers confident in AI's performance, down from 43% the previous year. This drop is attributed to AI's limited ability to maintain context over extended interactions, resulting in errors or 'hallucinations' when handling complex problems. The article suggests practical strategies for developers, such as opening new chats for distinct tasks and using AI for code generation rather than architecture. By treating AI outputs as drafts requiring human oversight and integrating tools like Stack Overflow for complex issues, developers can mitigate these limitations and enhance productivity. Despite advancements in AI, human expertise remains crucial for navigating complex coding challenges, as evidenced by the continued reliance on human resources for ethical and security-related coding concerns.