Architecting and Building Production-Grade GenAI Systems - Part 6: Model Management & Improvement

If you haven't read part 5, please click here to get started.

Model Management and Improvement

16. Feedback Loop

Establish a feedback loop for model performance monitoring and retraining. This can be built as part of the API, where you can expose methods for users to rate their interactions or completions received. You should also have established lines of communication with your developer users that can either report issues or submit feedback regarding the usability of the system. Performance wise we will leverage the logging and monitoring mentioned previously to make sure we detect any performance degradation and proactively look at any problems.

17. Updating the LLM Model

At some point you will either have a new version of the GPT model to enable in the backend or you might want to try the application with a different version of the Cognitive Search index that uses a different subset of documents from our data lake. 

We can leverage the CI / CD practices as well as the capabilities of the API management service to deploy a policy that will direct a portion of the traffic to the new LLM deployment and then use the logging and instrumentation to determine if the new version is better in terms of performance, quality of response, etc. and make sure there are no degradations.

Conclusion

Finally here’s a full view of our initial implementation:

In conclusion, building a production-grade LLM system involves a multidisciplinary approach, addressing architectural, ethical, security, and user experience aspects. By following best practices and learning from past experiences, you can create a powerful, responsible, and scalable AI system that serves your business and users effectively. 

The architecture we built in this post is also just the beginning, we didn’t explore for example, how to use data stored in relational databases or integrate with an existing warehousing system or how to enable an AI agent to take actions based on a service interaction. All these are more advanced use cases that we will explore in future posts.

Finally, remember that responsible AI development and ongoing monitoring are crucial for the long-term success of your LLM system. Stay informed about industry trends, regulations, and emerging technologies to keep your system up to date.