Everyone’s Building with LLMs Wrong. Here Are the 10 Myths Killing Your Projects.

Author(s): Mayank Bohra

Originally published on Towards AI.

Stop treating LLMs like magic solutions. After 8 months building production AI systems with Claude, GPT, and custom models, here’s what actually breaks when hype meets reality.

After spending months building production AI applications — not prototypes, not demos, actual systems that handle real user traffic — I’ve watched dozens of teams make the same fundamental mistakes. They fall for myths that sound convincing but destroy projects when they hit production.

The article discusses common misconceptions about large language models (LLMs) that can hinder the success of AI projects. It emphasizes the importance of understanding the limitations of LLMs, such as their lack of true comprehension, the unpredictability of outputs, and the need for effective context management. Through systematic approaches and a focus on real-world applications, the author argues that teams can overcome these pitfalls, leading to more reliable and successful AI systems.

Read the full blog for free on Medium.

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Published via Towards AI