Extensibility.md

Future Extensibility 

The current solution focuses mainly on post-call audio and text conversation knowledge mining. There are a number of additional capabilities and considerations that would complement this accelerator and are areas for extending: 

1. Improved system of aggregate insight prompts 

   The accelerator utilizes system prompts that are used to derive insights from a conversation. These are customizable within the accelerator configuration, but additional insights may be required based on new use cases for this accelerator, which will require changes to the existing prompts or additional prompts to be created. In addition, new techniques or approaches to the prompt structure may be made to enhance the accuracy and relevancy, especially as new versions of GPT are released.  

2. Q&A style interface for users to ask questions across all indexed conversations

   In the current accelerator, a keyword search via Azure Cognitive Search is the only way to filter and find conversations relevant to their inquiry. Using a Q&A approach would unlock a conversational approach and follow up questions. This could also be extended to allow users to derive insights on their own, without pre-processing across the full dataset. 

3. Power BI dashboard to interact with index tags and insights 

   As insights are derived and aggregated, a common approach is to pull these data points into a larger dashboard that integrates with additional systems and data. This could include call center specific dashboards and would allow these insights to be derived from AI to show the full picture with more standard call center KPIs. Given this solution is an example of how to mine these datapoints, a dashboard similar to this would be a common integration in a real-world implementation. 

4. Historical reprocessing workflow for all conversations to aggregate new insights as they're authored 

   It's common for additional insights or enhancements to prompts to be made over time, but as these changes are made the existing conversations will need to be reprocessed. Currently, the solution does not have a "reprocess" capability and would require the conversations to be removed and then readded. For ease of use, a reprocessing capability could be added to simply rerun the skills on the index either on a regular cadence, or manually running across the full index. This ensures most up-to-date insights and capabilities within the mined knowledgebase. 

5. Pre-processing mechanism for simple TXT conversions to the required JSON format 

   Text conversations are currently required to be in a single JSON format, but a pre-processing tool could be created to convert a simple text file into the correct JSON format. 

6. Video and other conversation formats 

   The solution only accepts text and audio based conversations, but this could be extended to support additional formats as well -- including video and images sets. This extends the ability to support additional channels customer service and users interact through, still mining the same knowledge insights into a single system. 

7. Opt-out flag on conversations to skip knowledge mining processing

   Privacy and legal requirement around data processing is a consideration that all data processing platforms should consider. This solution can be extended to support an explicit parameter or flag on conversations that users have opted out or in to their conversation being processed by ML and AI. The solution can then decide to skip over specific conversations where users have opted out.