Product problem considerations when building LLM applications

Dec 19, 2023 ·

Language models have become increasingly popular in recent years, due to their ability to generate text from a given set of words or phrases. However, there are a few key considerations that developers should be aware of when building and deploying language model applications.

First, it is important to consider the input type: will the model use natural language processing (NLP) technologies such as sentiment analysis and topic modeling, or will it accept raw text? The selection of an appropriate input type can make a big difference in the accuracy of the model’s results. Additionally, it is important to take into account the context of the input data. In some cases, the model may be trained on data from a particular domain, such as legal or medical text. It is important to ensure that the model is robust enough to work correctly with any type of input.

The size of the language model can also have a major impact on the accuracy of the system. Large models require more computational resources, making them more expensive to deploy. Smaller models may not be able to adequately capture complex relationships or nuances in the data. It is therefore important to strike a balance between cost and accuracy for the application.

Finally, it is important to understand the limitations of the model. Language models are not perfect; they are only as good as the data used to train them. Without access to a large, diverse dataset, the model may struggle to accurately predict natural language. Additionally, the model can only handle so much data before becoming overwhelmed. Developers should be aware of these limitations and design their applications accordingly.

In conclusion, language models are incredibly powerful tools for creating text-based applications. However, developers must consider the input type, model size, and limitations of the model when designing and deploying an application. By understanding these factors, developers can create applications that are both accurate and cost effective.