Practical considerations

Our tutorial focuses on the minimum factors required to facilitate testing, but here we discuss the complexity of an actual process.

Remember that the code snippets and api requests provided above are illustrative, offering a high-level overview. The actual implementation will depend on the specific requirements of your project. Prompts used here are for demonstration purposes; effective prompt generation often necessitates domain expertise and careful analysis.

Evaluating the consistency of generated text involves significant complexity. The combined use of various metrics in a single API request, as demonstrated, might not always be ideal. Depending on your specific use case, it may be more effective to evaluate each metric separately to gain a more detailed understanding of the performance and quality of the summaries.

Distributional's framework is designed to handle these complexities by providing a systematic approach to measure and analyze the stochastic nature of LLMs. This allows for the detection of non-stationary shifts in third-party applications, ensuring that changes or degradations in performance are identified promptly and accurately. By setting appropriate tests and thresholds for assertions, users can monitor and validate the consistency and quality of LLM outputs, supporting the ongoing maintenance and improvement of these models.