Unlike traditional web development, which focuses on building web pages and applications, LLM application development involves working with natural language prompts and integrating your data sources to create workflows. The LLM provides a natural language interface for your users to provide input and to generate natural language output. However, the LLM is limited. The content that is in it’s training set is all it knows. It’s the same for all users and isn’t very good outside those bounds.
If you want to build a differentiated LLM AI product you have to either a) use the model in a novel way (which can then be ripped off) b) bring a new dataset to it and do something more focused/domain specific than the general purpose LLM or c) build integrations on top of it that do useful stuff.
Define the problem: The first step in building an LLM application is to define the problem that the application will be solving. This includes identifying the specific task that the model will be trained on and the type of data that will be used to train it. This is easily the most important step. The more specific and focused you can make the problem statement, the better your chances for success are.