How does RAG work with LLM

In Retrieval-Augmented Generation (RAG), RAG and the LLM don't typically process the query in parallel. Here's how the process normally unfolds:

Query Processing: The user submits a query.

Query Encoding: The RAG system's query encoder takes the user query and transforms it into a vector representation, essentially capturing the meaning and intent of the prompt.

Retrieval: The retriever component utilizes this encoded query to search the knowledge base for documents that are most relevant. This retrieval process might involve searching through a vast collection of text data.

LLM Input Preparation: The retrieved documents are then preprocessed and potentially combined or summarized to create a suitable input for the LLM.

LLM Generation: Finally, the LLM receives the encoded query (sometimes along with the retrieved documents or their summaries) and generates a response based on its understanding of the query and the provided context.

Reasons for Sequential Processing:

Efficiency: Encoding the query and retrieving relevant documents can be computationally expensive. Processing these steps sequentially avoids overloading the system and ensures the LLM receives a well-defined query and context before generating a response.

Control Flow: The retrieved documents act as supplementary information for the LLM. Sequential processing allows for better control over the information the LLM receives and helps to avoid potential issues where irrelevant or misleading information from the knowledge base might negatively impact the LLM's output.

However, there are some ongoing research efforts exploring parallel processing in RAG systems. These approaches aim to improve efficiency by potentially encoding the query and performing retrieval concurrently. However, these techniques are still under development and require careful consideration of the trade-offs between speed and accuracy.

In conclusion, the standard RAG architecture employs a sequential approach where the query is encoded, relevant documents are retrieved, and then the LLM leverages this information to generate a response.

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Gemini