# Practical considerations 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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.dbnl.com/v0.23.x/tutorials/llm-text-summarization/practical-considerations.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.