Expanding the Capabilities of Generative AI with Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) represents an innovative advancement in generative artificial intelligence, enabling us to leverage data "beyond the model's scope." This refers to external information that was not integrated into the model during training. By integrating RAG, we can add essential guardrails to the generated output and minimize instances of hallucination. 

RAG offers valuable applications across various generative AI use cases, such as: 

• Question-answering systems
• Text summarization
• Content generation 

To better grasp RAG's functionality, consider a human interaction analogy. Imagine providing a document to an individual and requesting they generate an answer based on the information within that document.

The two primary components of RAG are: 

1. Retrieval step: In this stage, we search through extensive knowledge bases (documents, websites, databases, etc.) to identify data relevant to the model's instructions. 
   
   
2. Generation step: Similar to traditional generation use cases, this phase generates a response or content based on the information retrieved during the initial step. The crucial distinction lies in how the retrieved information is utilized – it serves as context and is incorporated into the prompt provided for the generative model.

In the realm of vector databases, unstructured data is transformed and stored as numeric representations, which are commonly referred to as embeddings for AI applications. Embeddings are derived by employing embedding models such as word2vec. Consider the illustrative example of a semantic search for the term "computer". Observe the closest matching words in the table below, with distances measured numerically since the search was executed utilizing vector (numeric) data. Feel free to test additional words on this platform. https://projector.tensorflow.org/