Constraints

Performance Constraints

• Latency: Interaction with cloud-based services like Azure Open AI may introduce latency issues, particularly if the user base is globally distributed relative to Azure's data centres.

• Scalability: While Azure services are generally scalable, the configuration and cost management of scaling to handle peaks in demand (especially during heavy document processing) need careful planning.

• Accuracy:

  • Document Analysis Accuracy: When using services like Microsoft Document Intelligence for processing and analysing documents, the accuracy of the outputs (e.g., extracted data, interpreted information) is crucial. Accuracy may vary depending on the complexity of the documents, the quality of the scans or formats, and the capabilities of the underlying AI models. Ensuring high accuracy is essential for the application to be reliable and trustworthy.

  • AI Response Relevance and Precision: For the component interacting with Azure Open AI for chat-based queries, accuracy not only pertains to correct information retrieval but also to the relevance and precision of the AI's responses based on user queries. The model's understanding of the context, its training data, and its ability to generalize from that data to answer queries accurately are critical. This is particularly challenging when the questions are based on the nuanced content of uploaded documents or require domain-specific knowledge.

  • Error Rate and Correction Mechanisms: A key aspect of accuracy involves how errors are handled and corrected within the application. This includes the ability to learn from incorrect responses and improve over time, either through machine learning techniques or through user feedback mechanisms that allow for manual corrections and refinements.