The author uses examples to illustrate the limitations of LLMs, such as the need for specific context and the inability to understand specialized problems. The author also emphasizes the need for manual programming of agents to get the most out of LLMs. The author concludes by stating that while LLMs can add value, their integration into applications will require significant effort and there will be a long tail of applications where AI can add value, suggesting a promising future for Applied AI companies.
Key takeaways:
- Large Language Models (LLMs) are not going to make AI-powered products obsolete. They require a lot of work to be integrated into products, including asking the right questions, providing the right context, and creating agent structures.
- LLMs are not Artificial General Intelligence and have limited abilities. They can provide useful information based on context, but require specific instructions and context to be truly effective.
- LLMs do not understand specialized problems. They are trained on public information on the internet, which is mostly in English, and may not be able to provide expert level answers in specialized contexts.
- Integrating AI into every aspect of a product is a lot of work. Each feature that requires AI integration will require manual work to provide the correct context and prompts for the LLM.