The article emphasizes the importance of balancing cost, engineering effort, and performance when using LLMs. It highlights the use of commercial LLMs for quick validation of ideas and early user feedback, but also notes the potential cost savings of self-hosting open-source or custom fine-tuned LLMs. The article concludes by predicting the growing importance of generative AI in solving large-scale, business-critical problems in the future.
Key takeaways:
- The process of building with Large Language Models (LLMs) involves stages like product ideation, defining requirements, prototyping, learning from small-scale experiments, and finally launching and deploying the product at scale.
- Identifying use cases for generative AI involves understanding the opportunities that the technology can address, particularly in areas that involve analysis or interpretation of unstructured content at scale, require massive scaling, or are challenging for traditional ML approaches.
- Prototyping AI applications involves selecting an appropriate LLM, creating the right prompt, and using AI-assisted evaluation to ensure the quality of results. Once confident in the results, a limited release of the product is rolled out to gather user feedback and make improvements.
- Deploying at scale involves considering trade-offs when designing the LLM inference server, such as balancing costs and engineering effort. Self-hosted LLMs can reduce costs but require additional development time and maintenance overhead.