Getting Unstructured Data RAG-Ready for LLMs

THE PROBLEM

There are significant challenges for organizations seeking to build custom Large Language Models (LLMs) tailored to their specific needs: the difficulty of providing these models with clean and accurate, company-specific retrieval-based data from their unstructured Data Estates often poses high risk. Organizations struggle to identify and locate pertinent custom-generated data that is relevant to their particular industry verticals. Standard classification models and manual methods fall short in effectively identifying, enriching, and classifying this custom data.

To address this issue, organizations need to be "RAG-Ready." "RAG-Ready" refers to a system's preparedness for Retrieval-Augmented Generation (RAG), a technique in natural language processing that combines retrieval-based and generation-based approaches. This technique ensures more accurate and contextually relevant responses by integrating:

• Retrieval-Based Approach: Fetching relevant documents or pieces of information from a large dataset to provide necessary context for the generation phase.

• Generation-Based Approach: Using advanced language models to generate coherent and contextually appropriate responses based on the retrieved information.

OUR SOLUTION

Zantaz Data Optimization addresses the challenge of efficiently identifying, enriching, and classifying unstructured data to ensure that organizations can provide their LLMs with clean and accurate retrieval-based data. This mitigates the risk of feeding LLMs with inaccurate or non-curated data sets, which is crucial for building effective custom LLMs tailored to specific organizational needs. Zantaz Data Optimization achieves this with key features such as:

• Local Secure Ring-Fenced Classification Models: Organizations can use a small training dataset (about 60-100 documents) to train the classification model. This model can then process and classify any filtered unstructured data sets, ensuring that only the relevant information is identified and enriched.

• Data Router for Seamless Data Integration: Using advanced language models to generate coherent and contextually appropriate responses based on the retrieved information. The Data Router ensures that only the classified data, deemed relevant and accurate, is used as retrieval-based data for feeding into LLMs. The classified and enriched data is seamlessly fed into the LLMs, ensuring that the models receive high-quality and contextually appropriate information.