Why? and How to Ground a Large Language Models using your Data? (RAG)

Large language models (LLMs) are versatile and can perform a wide range of tasks, but there is a concern that they may not always follow the objectives for which they were created. To tackle this, retrieval-augmented generation (RAG) can be used to provide contextual grounding and offer users a specific set of data for a prompt's response. RAG can work efficiently with vector or pre-existing SQL or No-SQL databases by passing the query response to a large language model, which can then rephrase it into text-based responses that closely resemble human language and structure. The RAG technique reduces the cost associated with other solutions such as fine-tuning, allows for supercharged data retrieval, and delivers better responses with semantic similarity. Using RAG with your data stream can uplift the performance of your recommendation system. The article provides a detailed explanation of RAG with examples and guides to integrate RAG into your system for seamless operation.

Please note that vector databases are commonly used with RAG because they store vector embeddings, which can retrieve semantically matching results, whereas a complete text search strips context off the queries and only matches text. You can use any embedding model to make vector embeddings, and it is adaptive to various data types.

The article offers detailed instructions on how you can implement RAG with vector databases and pre-existing databases and provides additional resources for related topics for further reading.