Implementing a continuously improving system
Let’s implement monitoring and continuous improvement for our web page Q&A application powered by the LLM we’ve trained in previous chapters. Initially, the model provided basic answers to frequently asked questions but struggled with more nuanced queries and user-specific issues. Let’s improve this by incorporating a continuous improvement journey, integrating robust human feedback mechanisms, and closely monitoring performance metrics to refine the model iteratively.
Metrics used and performance improvements observed
When we began, the model’s accuracy in delivering correct answers was around 70%. With continuous feedback and iterative training, we’ve seen substantial gains, with accuracy improving to 92%. Similarly, precision, which gauges the relevance of the model’s answers to posed questions, has improved significantly from 65% to 90%. Furthermore, user satisfaction, as measured through...
