Knowledge-Augmentation Methods for Building LLM Applications

In the rapidly evolving field of artificial intelligence, Large Language Models (LLMs) have emerged as powerful tools for understanding and generating human-like text. These models, trained on vast amounts of data, have demonstrated remarkable capabilities in a wide range of applications, from writing assistance to answering complex questions. However, despite their impressive performance, LLMs are not without limitations. One of the most significant challenges is ensuring that the information they provide is accurate, up-to-date, and contextually relevant. Also, these LLMs will not have knowledge of your proprietary data or domain-specific knowledge (like acronyms). Also, There is always a knowledge cutoff to the pretraining data. So if you ask question on data which it is not trained on, it will not be able to answer your question. Below is a very simple illustration of the same on OpenAI’s chatgpt.

Knowledge-augmentation methods have been developed to address these challenges by enhancing the base knowledge of LLMs. These methods aim to supplement the pre-existing information encoded in the model’s parameters with external, structured data sources or real-time information. By doing so, they enable LLMs to provide more precise and current responses, which is crucial for tasks that require the latest information or domain-specific expertise.