Parameter Optimization for RAG Implementation: RAG for RAG

Retrieval-Augmented Generation (RAG) chatbots have been around for quite some time now and it has evolved with so much advancement in its implementation, from simple RAG to Simple RAG with Memory, Adaptive RAG, HyDe, Corrective RAG, Self RAG, Agentic RAG and much more. 

It is a technique that enhances text generation by incorporating real-time data retrieval allowing models to search external databases or documents during the generation process to provide more accurate and up-to-date response and eliminate/minimize hallucination. 

The focus is clearly on accuracy, which depends on what kind of and how many documents are needed, retrieved and presented to the model for generation. With this the evaluation strategy has also been evolving. Manually testing the sample set of responses against a standard set of responses to enabling the architecture itself to evaluate its response as in case of Corrective RAG (CRAG, https://arxiv.org/pdf/2401.15884) and Self RAG, the retrieved documents are given confidence score based on the relevance and based on confidence score certain actions are triggered. 

However, can we move a step back and evaluate the hyperparameter which are set for the RAG implementation like top-k, top-p, chunk size, overlap, temperature, etc., and adjust them with minimal trials to generate the best response? 

Meta-learning, a concept of ML where models are trained to “learn how to learn”. One aspect of it is, models learn from the outputs or metadata of other machine learning models, sometimes including hyperparameter tuning and algorithm selection. 

On the similar pattern, a meta-learning model that will learn from a database of past RAG use cases, which we have already implemented. These implementations will definitely have characteristics like domain, query complexity, data source, feedback. These can be mapped to the optimal parameter settings used for the said implementation. 

The exercise will involve gathering historical data from our RAG deployments, including query types, data formats (e.g., PDFs, structured documents), and successful parameter configurations (e.g., chunk size, top-k). Use this data to train a model to predict ideal parameter values based on use case similarity. The expected outcome will be initial settings like chunk size of 512 tokens, top-k = 10, etc. Ideally, it should reduce trial and error by using the prior implementation, enabling shorter delivery cycle for new scenarios.

#RAG #Tuning #Hyperparameter