Applicability of an AI Chat Assistant in Resolving Knowledge Compartmentalization Within a Workplace

This report aims to investigate whether AI Innovations can help manage complex operations of a company suffering from compartmentalized knowledge, where various teams need to rely on the broader business for information and guidance which is hard to come by individual specialists. An efficient and scalable cloud LLM application is proposed, where staff could query and receive responses that are grounded in facts derived from company’s data to which the GPT model has access.

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Cloud-Based LLM Architecture

Such innovations will necessarily be applied during the company’s ongoing transformation, part of which is to utilise AWS Cloud services, and so the artifact developed in the second part of this report showcases a scalable AWS LightSail Instance providing a cost-effective VPS for hosting the application’s WordPress front-end and PHP back-end server.

AWS API Gateways communicate with AWS Lambda serverless functions which store the OpenAI API Keys and interact with the model. The response fetched is then sent back to the front-end, showing the entire end-to-end process required to host a secure LLM app over cloud technologies.

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Data Security and Document Querying

Data Security and Privacy is considered should OpenAI become a third-party data processor for the company. A Document store, LangChain, is outlined as a framework that could facilitate tokenization of company data and storage in a Vector Store, so that the query sent to the OpenAI API contains relevant snippets of documents in the form of word embeddings.

A simple demonstration of ChatGPT-4.0 document reading capability is shown as an example of a typical use case within the company.

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Conclusion

This project demonstrates that LLMs work very well with cloud technologies and can be implemented with ease, providing access to cutting-edge technologies to any employee regardless of their technological acumen.