Best Practices for Quality Assurance in Multi-Layered LLM Systems (Frontend-Backend-Cloud)
DOI:
https://doi.org/10.56830/WRBA11202504Keywords:
Large Language Models, Quality Assurance, Frontend Testing, Backend Integration, Cloud Infrastructure, Multi-layered ArchitectureAbstract
This paper investigates good practices of quality assurance on multi-layered LLM systems, covering issues over frontend, backend, and cloud infrastructure layers. The research points out that the interfaces within architectural layers are the site for 78% of all critical failures, as opposed to the models themselves.
The paper specifies a series of peculiar QA challenges in LLM systems, including probabilistic testing paradigms for outputs depending on probability and latency issues, security vulnerabilities, and ethical considerations. It presents layer-specific best practices:
There is a UI testing and prompt engineering validation in the frontend QA, while the backend QA is all about API integration testing and model version compatibility and cloud infrastructure QA is about scaling, deployment verification, and disaster recovery.
The paper also presents the end-to-end testing strategies, measure of evaluation, and quality standards. It concludes that it is important to have comprehensive QA frameworks when designing reliable, efficient and trustworthy LLM applications whilst ensuring minimal technical and reputation risks.
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