Creating a private version of ChatGPT tailored to your own data is a fascinating concept that's gaining traction with the rise of Large Language Models (LLMs) like GPT-4. The idea is to harness the power of these advanced AI models while overcoming the limitations of fine-tuning with proprietary data, such as the risk of generating incorrect information and the inability to trace answers back to their sources.

To achieve this, one must architect a system that separates the knowledge base from the language model. This separation allows for real-time provision of accurate information without the need for retraining the model, thus saving on costs and ensuring data privacy. The process involves chunking and indexing documents to create a searchable knowledge base that the language model can query to find the most relevant information.

The key steps include:

1. Chunking Data: Breaking down documents into manageable pieces that respect the token limits of the LLM.
2. Building a Search Index: Creating a semantic search index to find the most relevant document chunks in response to user queries.
3. Prompt Engineering: Crafting concise prompts that guide the LLM to provide accurate answers without "hallucinating" or making up information.
4. Leveraging Existing Tools: Utilizing platforms like Azure Cognitive Search or OpenAI's embedding models to build your semantic search index.

By following these guidelines, you can create a Q&A engine that leverages the semantic understanding of LLMs like ChatGPT while ensuring that the answers are based on your specific data set. This approach not only enhances the accuracy and relevance of the information provided but also maintains the integrity and confidentiality of your data.

For those interested in delving deeper into the technical aspects, there are resources and projects available that can serve as a starting point for building such a solution. These include the Azure OpenAI Service, ChatGPT Retrieval Plugin, and various libraries that facilitate the integration of LLMs with other data sources and computational tools.

In conclusion, while fine-tuning LLMs with your data has its drawbacks, separating your knowledge base from the language model and using the right architectural and design patterns can help you build a powerful and private Q&A engine tailored to your specific needs.