Case Study
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The project's main objective was to develop a customized implementation of the GPT 4 Large Language Model for a Government Agency, with the purpose of creating an AI chatbot capable of providing information to Government officials and interacting with citizens to offer support and assistance. By creating this communication tool, the project aimed to improve efficiency and customer satisfaction while reducing the workload of the Agency.
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. Overall, successfully incorporating contextual knowledge into GPT-like models requires a balance between accuracy, efficiency, and relevance to the task.
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.
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.
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Context Embedding
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Deployment of In-House ChatGPT Tool
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Mar-Apr 2023
Apr -May 2023
