Public Preview of Azure OpenAI Service for Enhanced Data Management
Explore Azure OpenAI Service for private ChatGPT creation using personal data, addressing potential fine-tuning issues and utilizing knowledge base for accurate
Introducing Azure OpenAI Service On Your Data in Public Preview In this article, Mick Vleeshouwer explores the feasibility of creating a private ChatGPT model for Q&A using one's own data. He outlines the architecture and data requirements for this, and also discusses the limitations of fine-tuning pre-trained models. Some of the drawbacks of fine-tuning include problems with factual correctness, traceability, access control, and costs. To overcome these, Vleeshouwer suggests separating the knowledge base from the language model, thereby ensuring users receive accurate answers based on relevant data.
Key steps in the process involve:
- User asking a question.
- The application finds relevant text that likely contains the answer.
- A prompt with the relevant text is sent to the large language model.
- The user receives an answer or 'No answer found' response.
He also emphasizes the importance of context, recommending the use of a knowledge base for finding relevant documents via semantic search. This involves breaking down data into smaller, manageable pieces.
Azure OpenAI Service on your data supports connecting to multiple sources, including:
✅ Azure Cognitive Search index.
✅ Azure Blob storage container.
✅ Local files
Read the full article Introducing Azure OpenAI Service On Your Data in Public Preview
How to create a private ChatGPT with your own data
In this article, Mick Vleeshouwer explores the feasibility of creating a private ChatGPT model for Q&A using one's own data. He outlines the architecture and data requirements for this, and also discusses the limitations of fine-tuning pre-trained models.
- 1. User asking a question.
- 2. The application finds relevant text that likely contains the answer.
- 3. A prompt with the relevant text is sent to the large language model.
- 4. The user receives an answer or 'No answer found' response.
With Azure OpenAI Service, it is now possible to create tailored Q&A systems that leverage your organization's data for accurate and relevant responses to user queries. By separating the knowledge base from the language model and implementing semantic search, you can overcome common limitations of fine-tuning pre-trained models, such as issues with factual correctness and traceability. This approach provides both a more secure and cost-effective solution for harnessing the power of large language models in a way that meets your organization's unique needs.
Learn about Introducing Azure OpenAI Service On Your Data in Public Preview
Azure OpenAI service is a public preview tool that can be used to create a private ChatGPT model for Question and Answer (Q&A) purposes. This model is created by fine-tuning pre-trained models, however there are drawbacks to this approach, such as problems with factual correctness, traceability, access control, and costs. To tackle these issues, Vleeshouwer suggests separating the knowledge base from the language model, thereby ensuring users receive accurate answers based on relevant data. The process for creating a Q&A model includes the user asking a question, the application finding relevant text, a prompt with the relevant text being sent to the large language model, and the user receiving an answer or 'No answer found' response. Context is also important, and Vleeshouwer suggests using a knowledge base for finding relevant documents via semantic search, which involves breaking down data into smaller, manageable chunks.
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Keywords
Microsoft Azure, OpenAI Service, ChatGPT, Fine-Tuning, Knowledge Base, Semantic Search