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What Web Developers Really Think About AI in 2025
As expected, **hallucinations** and other inaccuracies were the big one: after all, it doesn't matter how cheap, fast, or convenient a model is if you can't trust its output. Another common issue was **context limitations**, which becomes especially relevant when you try to apply these models to large existing codebases, as opposed to using them to prototype new ideas.
Related Pain Points2件
AI Agent Hallucination and Factuality Failures
9AI agents confidently generate false information with hallucination rates up to 79% in reasoning models and ~70% error rates in real deployments. These failures cause business-critical issues including data loss, liability exposure, and broken user trust.
Limited Contextual Understanding in AI Agents
6AI agents lack contextual understanding needed for long-form content and domain-specific nuance, reducing their effectiveness in handling complex scenarios that require deep understanding of broader context.