A Practical Quality Assurance Framework for Large Language Model Applications: Evaluation Metrics, Failure Modes, and Testing Strategies

Abstract:The rapid adoption of Large Language Models (LLMs) has created a critical need for systematic quality assurance. Unlike traditional software testing, LLM outputs are non-deterministic and require multi-dimensional human evaluation. This practitioner paper proposes a lightweight, actionable QA framework designed for AI Quality Analysts. The framework evaluates responses across five dimensions: (1) Factual Accuracy, (2) Instruction Following, (3) Completeness & Relevance, (4) Safety, Bias & Privacy, and (5) Linguistic Quality. We define 18 recurring failure modes observed in production LLM systems, including hallucinated citations, partial compliance, over-refusal, stereotype reinforcement, and contradictory statements. We present a 5-point anchored rubric and a defect triage process using severity levels (High/Medium/Low/N/A) and a standardized bug log format. The framework was empirically validated on 4 diverse test cases: (a) summarization of a complex live news blog, (b) structured JSON generation for breakfast ideas, (c) bias-free job description generation for a nurse, and (d) mathematical reasoning with steps. Results showed a 25% defect rate, with primary failure mode being hallucination on unverified live news (Medium severity), while structured, bias, and reasoning tests passed perfectly (N/A severity). This framework enables QA teams to bring structured, repeatable quality practices to LLM evaluation without requiring large-scale compute. All evaluation templates are provided as supplementary material for immediate use in industry QA workflows. Keywords: AI Quality Assurance, LLM Evaluation, Data Quality, Hallucination Detection, Responsible AI, Model Validation, Human Evaluation, AI Quality Analyst Author: Neil George, Independent ResearcherVersion: v1.0 - Initial practitioner release for portfolio and community feedbackLicense: CC-BY 4.0Supplementary Files: LLM_QA_Evaluation_Template_4_Tests.xlsx contains 4 scored test cases with evidence.Intended Use: Portfolio for AI Quality Analyst role and open-source QA template.