This text discusses the integration of ChatGPT with internal knowledge management systems in order to enhance the ability of the chatbot to provide accurate and relevant information. It highlights the importance of internal knowledge management in a post-pandemic world with hybrid working and higher employee turnover. The text explains the concepts of ChatGPT, GPTs, and Large Language Models (LLMs) and their applications in natural language processing tasks. It presents the case for customizing ChatGPT through fine-tuning and in-context learning methods, comparing their advantages and limitations. The text then introduces the concept of Prompt Engineering and Retrieval Augmented Generation (RAG) as a method to enhance ChatGPT’s answers by providing additional context from a knowledge base. The implementation of RAG Q&A in Python is discussed, along with the process of building a vector database using FAISS for fast retrieval and similarity search. The text concludes by mentioning future enhancements such as integration with messaging platforms and Q&A platforms, enhancing data security, and exploring multimodal capabilities of language models.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Integrating ChatGPT with internal knowledge base and question-answer platform | Bringing the power of ChatGPT to internal knowledge management | Improved internal knowledge management with ChatGPT | Hybrid working and higher employee turnover |
| ChatGPT’s limitations in providing accurate answers | Enhancing ChatGPT’s answer quality with additional context | More accurate and context-aware answers from ChatGPT | Improving user experience and reducing bot hallucinations |
| Using prompt engineering and retrieval augmented generation | Improving the process of answering user queries with relevant information | Automated retrieval of relevant documents for better answers | Enhancing the accuracy and relevance of ChatGPT’s responses |
| Building a vector database for fast retrieval and similarity search | Utilizing vector databases for storing and retrieving documents | Efficient and comprehensive document retrieval | Enabling faster and more accurate search capabilities |
| Integration of ChatGPT as a Telegram or Slack bot | Creating bots for easy access to ChatGPT on messaging platforms | Convenient and accessible knowledge management through messaging apps | Improving accessibility and usability of ChatGPT |
| Integration with an internal Q&A platform | Incorporating ChatGPT with an internal Q&A platform | Building a centralized knowledge base with user-contributed questions and answers | Enhancing the knowledge base and improving ChatGPT’s capabilities |
| Enhancing data security for sensitive internal knowledge | Exploring secure hosting options for LLM models in a Government cloud | Secure hosting of LLM models for sensitive data | Ensuring data security and compliance with regulations |
| Advancements in LLM-powered applications | Continued development and exploration of LLM-powered applications | Increased applications and capabilities of LLM models | Harnessing the potential of LLMs for various tasks and domains |
| Ethical and social considerations in language models | Addressing ethical and social risks associated with language models | Responsible and safe advancement of AI technologies | Balancing the benefits and risks of language models and ensuring ethical use |