Generative AI - Document Retrieval and Question Answering with LLMs

Apply LLMs to your domain-specific data

With Large Language Models (LLMs), we can integrate domain-specific data to answer questions. This is especially useful for data unavailable to the model during its initial training, like a company's internal documentation or knowledge base.

The architecture is called Retrieval Augmentation Generation or less commonly used Generative Question Answering.

This article helps you understand how to implement this architecture using LLMs and a Vector Database. We can significantly decrease the hallucinations that are commonly associated with LLMs.

It can be used for a wide range of use cases. It reduces the time we need to interact with documents. There is no need for us anymore to search for answers in search results. The LLM takes care of precisely finding the most relevant documents and using them to generate the answer right from your documents.

Jump to the Notebook and Code

All the code for this article is ready to use in a Google Colab notebook. If you have questions, please reach out to me via LinkedIn or Twitter.