More and more, people are building products as thin wrappers over other models, especially LLMs. This can cause major issues, and there is a better way.

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To build unique, valuable, and fast AI products, it is recommended to avoid just wrapping over other models. This approach faces problems such as lack of differentiation, high costs of running large language models, slow performance, and limited customization. Instead, developers should create a toolchain that combines fine-tuned models, custom compilers, and custom-trained models. By using AI models selectively and focusing on specific problem areas, developers can achieve faster, more reliable, and differentiated AI solutions.

Don’t Build AI Products The Way Everyone Else Is Doing It

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