Retrievable-Augmented Generation : It's a technique that enhances the accuracy and factual grounding of generative models by incorporating external information retrieval:

• Imagine a courtroom judge: They base their decisions on legal knowledge and arguments presented, not just general understanding.

• Similarly, RAG equips generative models (like large language models) with context and facts retrieved from external sources like databases, documents, or web searches.

Here's how it works:

1. The user provides a prompt or question.

2. The RAG system uses retrieval techniques to search for relevant information from external sources based on the prompt.

3. The retrieved information (like relevant documents or snippets) is then fed into the generative model.

4. The generative model processes the retrieved information along with its own internal knowledge to generate a response.

The process involves the breaking down the content within a document into segmented token data chunks. This process is called tokenization. In RAG, the document is tokenized into smaller units, such as words or sub-words, which are then used to create a representation of the document. This representation can then be used to retrieve similar documents or to generate new text that is similar to the original document.

This approach offers several advantages:

• Improved accuracy and factuality: By using external knowledge sources, RAG models can provide more accurate and reliable information compared to traditional generative models that solely rely on their internal statistical patterns.

• Context-awareness: The retrieved information helps the model understand the context of the prompt, leading to more relevant and coherent responses.

• Flexibility: RAG models can be adapted to different domains by using different information retrieval sources.

However, RAG also comes with some limitations:

• Computational cost: Searching and processing external information can be computationally expensive.

• Data quality: The accuracy of the model heavily relies on the quality and relevance of the retrieved information. How to optimise your data for RAG...

Overall, RAG represents an exciting advancement in generative AI by combining the strengths of retrieval and generation techniques. It allows for the creation of more informative, factual, and context-aware responses from generative models.

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