Retrieval-Augmented Generation (RAG) Production System - Data Relevancy Improvement Technique

Today all organizations are adopting to the wave of Generative AI - Large Language Model (LLM) based applications into their business domain.

Most popular use-case currently implemented based on Retrieval-Augmented Generation (RAG).

Why RAG Use-Case Arrived ?

Generative AI uses large language models (LLMs) to create text responses. The generated text is often easy to read and provides detailed responses that are broadly applicable to the questions asked of the software. However, the information used to generate the response is limited to the information used to train the AI, which may be weeks, months, or years out of date. This can lead to incorrect responses that erode confidence in the technology among customers and employees

What Is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) is a technique that optimizes the output of a large language model (LLM) with targeted information without modifying the underlying model itself. This targeted information can be more up-to-date than the LLM and specific to a particular organization and industry. RAG can provide more contextually appropriate answers to prompts as well as base those answers on extremely current data. The concept of RAG was introduced in a 2020 paper by Patrick Lewis and a team at Facebook AI Research

• RAG is a relatively new artificial intelligence technique that can improve the quality of generative AI by allowing large language model (LLMs) to tap additional data resources without retraining.
• RAG models build knowledge repositories based on the organization’s own data, and the repositories can be continually updated to help the generative AI provide timely, contextual answers.

Key Concepts & Technologies Used To Design & Build RAG System :

1. Sentence Embedding Model : Embedding models especially Deep neural network (DNN) models and Transformer models used to create sentence embedding which are numerical representation of text containing all the contextual, syntactical, semantic knowledge.
2. Vector DB : Vector Databases are used to store and index sentence embeddings.
3. Retrieval Algorithm : These algorithms are used to extract most relevant information for synthesizing information from the knowledge bank using Generative AI Large Language Models (LLM).

Why Retrieval-Augmented Generation (RAG) System Fails ?

One and only reason for failure of RAG systems are because of Similarity vs Relevancy battle.

Inside any production ready large scale based Retrieval-Augmented Generation (RAG) System data Relevancy in extraction of information from Vector Databases plays a vital role.

How To Improve Data Relevancy In Retrieval-Augmented Generation (RAG) System ?

While extracting knowledge based on Retrieval Algorithm always it's a good practice to check relevancy of data extracted by projecting the embedding vectors into 2D or 3D representations.

Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for projections into 2D visualisation

Best way to improve relevancy results using Cross-Encoder Re-Ranking Technique.

Cross-Encoder : A cross-encoder is a type of neural network architecture used in natural language processing tasks, particularly in the context of sentence or text pair classification. It is designed to evaluate and provide a single score or representation for a pair of input sentences, indicating the relationship or similarity between them. This is different from other architectures like siamese networks or traditional encoder-decoder models

Re-Ranking : After retrieval of relevant results all results are re-ordered and shuffled with right compatibility and relevancy with the query.

Try development of re-ranking techniques using pre-trained or custom trained encoder models. Let's connect for consultancy to know more about development of RAG systems.

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