TrueNorth Group - Prosum Knowledge Retrieval Engine

Introduction

The Prosum’s Knowledge retrieval engine is a cutting-edge solution revolutionizing the way organizations access and utilize vast repositories of information. Built upon state-of-the-art Azure technologies, it represents a breakthrough in information retrieval, seamlessly blending advanced language processing with efficient data mining capabilities.

By harnessing the power of machine learning, our Knowledge Retrieval LLM offers unparalleled accuracy and speed in retrieving, summarizing, and contextualizing information from diverse sources. Whether its uncovering insights buried within extensive documents, providing instant answers to complex queries, or delivering personalized recommendations from company documentation, this Knowledge retrieval engine empowers users to navigate the wealth of knowledge at their fingertips with ease and efficiency.

The Knowledge retrieval engine seamlessly integrates with a multitude of endpoints, including Microsoft Teams, custom websites, mobile applications, CRM systems, and more. Its unique coding framework allows for easy customization to cater for specific client requirements.

Critical Challenges

The design of the knowledge retrieval engine aims to tackle numerous critical challenges across diverse domains, with the challenges encompassing the following:

Ø Information retrieval and summarization

Ø Task Automation

Ø Knowledge Discovery

Ø Language Translation

The Solution Approach

The knowledge retrieval engine integrates both internal and external data sources within the Retriever Augmented Generation (RAG) framework to ground the Large Language Model (LLM) and enhance overall performance. Its primary goal is to facilitate swift and efficient retrieval of requested information for internal staff or a company's external client base, addressing challenges of time-consuming and inefficient data sifting. By implementing this solution, companies can redefine how they extract value from their data in a competitive and fast-paced environment.

Constructed using the Azure technology stack's REST APIs, the knowledge retrieval engine incorporates components like Azure Document Intelligence for extracting information from various document formats and Azure AI Search for optimizing document indexing and search using vector embedding and semantic search techniques. Azure AI Studio's LLM modelling capabilities are leveraged for configuring, training, fine-tuning, and deploying the chat models. Additionally, Azure Web Applications are deployed to present the chatbot solution through a web interface, serving as the interaction layer between the client and the chatbot, with customization managed on a per-client basis.

A successful implementation hinges not only on the adoption of individual technologies or frameworks like prompt chaining or RAG but on a balanced, multi-pronged approach integrating all these elements to deliver a comprehensive solution. Throughout the implementation process, a test and learn framework is employed to optimize the chatbot according to the clients' specific needs and requirements.

Product Features

Prosum's knowledge retrieval engine offers a roadmap for deploying an LLM model that yields tangible results and enhances efficiency within corporate settings. Key product features include:

Ø Data and workflow orchestration

Ø Prompt and Parameter Optimization

Ø Model Customization

Ø Model reliability and ethics

Ø Model serving and integration

Implementation

The usual implementation timeframe spans approximately 3-4 months, subject to the complexity of the client's data sources. Implementation involves configuring the environment, ingesting, processing, and parsing data sources. Following this, tasks include documentation indexing, designing system messages, and optimizing prompts. Subsequently, Parameter and Prompt grid search optimization, model fine-tuning, automation, deployment, and integration take place. The implementation plan further encompasses model monitoring and maintenance services, documentation, as well as regular updates and ecosystem enhancements.