Retrieval-augmented generation (rag) system optimization

An apparatus comprising:

• at least one processing device configured to:
• obtain training data for a retrieval-augmented generation (RAG) architecture comprising a retriever model and a generative model, the retriever model configured to identify information chunks relevant to input queries, the generative model configured to generate outputs based on the information chunks and the input queries;
• generate a prompt for the generative model;
• generate multiple sets of queries for the retriever model, each query in the multiple sets of queries configured to cause the retriever model to select a set of information chunks associated with the prompt;
• generate multiple responses to the prompt using the generative model and the sets of information chunks;
• determine rewards associated with the RAG architecture based on the responses;
• and
• train the generative model based on the training data and the rewards to produce an updated RAG architecture.

A non-transitory computer readable medium containing instructions that when executed cause at least one processor to:

• obtain training data for a retrieval-augmented generation (RAG) architecture comprising a retriever model and a generative model, the retriever model configured to identify information chunks relevant to input queries, the generative model configured to generate outputs based on the information chunks and the input queries;
• generate a prompt for the generative model;
• generate multiple sets of queries for the retriever model, each query in the multiple sets of queries configured to cause the retriever model to select a set of information chunks associated with the prompt;
• generate multiple responses to the prompt using the generative model and the sets of information chunks;
• determine rewards associated with the RAG architecture based on the responses;
• and
• train the generative model based on the training data and the rewards to produce an updated RAG architecture.