The most popular and widely known example of an LLM app is ChatGPT. Have you ever thought about how one of these LLM apps is made? Let me help you get through this! According to Alireza Goudarzi and Albert Ziegler, it can be covered in five steps.
- Focus on a Single Problem: Humans know that when we try to tackle too many problems at once, we often don't get good results and can end up frustrated. The same goes for models. If focused on a single problem, models will learn faster, and progress can be seen more quickly. For example, GitHub Copilot cleverly focuses on coding functions within an IDE.
- Choose the Right Pre-trained LLM: If you want to build an LLM app for commercial purposes, follow these steps to select the right pre-trained model:
- Customize Your LLM: Customizing a pre-trained language model involves adapting it for specific tasks, such as generating text on a particular subject or in a distinct style. Here are some methods to customize your LLM:
- Set Up Your App’s Architecture: Setting up an app requires different components, which can be divided into three categories:
- Evaluation: These evaluations are termed “online” because they assess the LLM's performance during user interaction. For instance, online evaluations for GitHub Copilot are measured by the acceptance rate (how frequently a developer accepts a suggested completion) and the retention rate (how often and to what extent a developer edits an accepted completion).
Good luck with creating your first LLM app!
