RAG (Retrieval Augmented Generation) is a technique that enhances the contextual awareness of large language models by feeding them information relevant to your prompt.
This method proves more effective for ensuring relevance than traditional fine-tuning techniques.
Imagine you have hundreds of documents, many of which reference a specific technique, such as engineering an airplane wing.
RAG employs semantic search to identify all pertinent content related to your search term, regardless of the document it resides in.
This efficiency stems from the way data is organized in a vector database, where similar linguistic information is clustered closely in vector space, allowing the system to disregard file boundaries.
Relevant data snippets are then extracted from these documents and incorporated into your prompt before it is processed by the large language model.
While the intricacies of the process are vast, the bottom line is that your response becomes significantly more insightful and tailored to your needs.
