Use Case Overview

In the rapidly evolving landscape of the public sector industry, the demand for accurate, timely, and efficient document processing is more critical than ever before. Traditional information retrieval and response methods are no longer sufficient to meet the complex challenges faced by government agencies, public service organizations, and institutions.
To address these challenges, our cutting-edge Question-Answering using Retrieval Augmented Generation and Large Language Models solutions are tailored specifically for the unique needs of the public sector.

Challenges/Pain Points

The Large Language Models as standalone solutions have several shortcomings that can be mitigated. To name a few:

• Out-of-date responses – Large-Language Models (LLMs) are bound to the data they were trained on and may produce outdated responses if not updated and retrained frequently.
• Lack of industry – specific knowledge – Generic Large-Language Models (LLMs) do not have domain-specific knowledge needed to provide contextually specific responses.
• High training costs for frequent knowledge updates – The large-scale nature of Large-Language Models (LLMs) results in costly and resource-intensive training requirements for frequent knowledge updates.
• Hallucinations – Even when fine-tuned, Large-Language Models (LLMs) can “hallucinate” or generate factually incorrect responses not aligned with the provided data.

Solution

Question-Answering using Retrieval Augmented Generation and Large Language Models solutions solves the challenges listed above that every public sector will face when implementing Large-Language Models (LLMs).

• Question-Answering – using Retrieval Augmented Generation and Large Language Models keeps the model updated with the latest data, and to leverage this, our robust pipeline that automates the process of updating the data source periodically can be an effective solution.
• Contextual understanding and industry-specific knowledge – Our pipelines enhance LLMs’ contextual understanding and industry-specific knowledge by integrating systems that can access external knowledge bases allowing the models to retrieve relevant information beyond their training data.
• Efficient Computation and Reduced Latency – Our pipelines reduce the high computational costs of LLMs and lower latency with smaller, more efficient models while delivering higher-quality responses with significantly less computational overhead.
• Mitigating bias and improving fairness to address Hallucinations – Our retrieval mechanisms enable more diverse information retrieval, offering multiple perspectives. Moreover, the explicit control over information sources provided by the systems allows for a curated, diverse document set, reducing the influence of biased sources.

Benefits

• Quickly retrieve information – Having a question-answering pipeline means that manually searching through documents is reduced and information can be gathered from existing knowledge bases much faster.
• Easily reference sources – Question-answering pipelines also enable users to access the documents used to synthesize answers and cite sources of information.
• Boost productivity – Because of the increased retrieval of information, employees spend less time familiarizing themselves with company tools and information so they spend less time onboarded.
• Create dynamic FAQs – Instead of creating static FAQ pages. Companies can create dynamic question-answering responses that cover more questions and increase customer engagement and sales.