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Case Study

Large Language Model Implementation

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Overview

The project's main objective was to develop a customized implementation of the GPT 3 Large Language Model for a Government Agency. The purpose of the project was to create an AI chatbot capable of providing information to Government officials and interacting with citizens to offer support and assistance. With the deployment of this tool, the government agency aimed to improve efficiency and customer satisfaction while reducing the workload of the agency’s employees.

Challenge

Incorporating contextual knowledge into GPT like models require a lot of fine-tuning and careful consideration of the type of knowledge that is being incorporated. This requires a balance between accuracy, efficiency, and relevance of the documents ingestion to the task.

Problem

Approach

We begin by ingesting contextual data in various formats such as PDFs, lists, and text files. This data is then passed through an embedding layer, which generates context for queries. Using this contextual information, we deploy a chatbot model capable of handling inquiries from citizens, leveraging the additional context provided by the embedding layer while limiting the scope to a specific domain.

Embrancing Natural Language Models

At DareData, we specialize in offering personalized AI solutions that incorporate the unique requirements of businesses. Our primary focus is on incorporating private context into GPT-like models, allowing companies to have their own version of ChatGPT-like tools. With the ability to retrain or utilize pre-generated context, we enable organizations to harness the power of AI while maintaining data privacy. Our goal is to provide you with AI solutions that empower your company to have personalized tools, tailored specifically to your needs.

Time Line

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Collecting and Pre-processing Documents

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Context Embedding

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Deployment of In-House ChatGPT Tool

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Guardrail Development

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Key Insights

Incorporating Contextual Knowledge: The project highlights the challenge of successfully incorporating contextual knowledge into GPT-like models. This process requires careful fine-tuning to strike a balance between accuracy, efficiency, and relevance to the task at hand.

Personalized AI Solutions: The project aims to provide customized AI solutions tailored to the specific needs of businesses. By enabling companies to have their own version of ChatGPT-like tools, organizations can leverage AI technology while maintaining data privacy and enhancing efficiency.