LLMs are terrible at designing AI systems (www.seangoedecke.com)

Recent observations have revealed that large language models (LLMs) struggle significantly when tasked with designing AI systems, particularly those that involve other LLMs. In a notable example, an LLM was unable to propose an effective state-of-the-art solution for a memory tool intended to identify contradictions; instead, it suggested a complicated symbolic system, which is fundamentally flawed given the nature of natural language processing. This incident highlighted a critical issue: despite LLMs’ capability to excel in traditional programming tasks, they often misapply their understanding when confronted with AI-related problems. This limitation is crucial for the AI/ML community as it underscores a broader challenge in software design by AI agents. While LLMs can perform tasks that were difficult just a few years ago, they tend to default to outdated techniques instead of leveraging their potential for innovative solutions. Possible explanations include the outdated nature of the training data, insufficient reinforcement learning environments to encourage LLMs to call other models, and an inherent difficulty in software design by current AI systems. Ultimately, understanding these weaknesses will be key for developers to maximize LLM effectiveness while minimizing wasted time and resources on ineffective approaches.