Large Language Models (LLMs) are increasingly being incorporated into software-development workflows for code generation, test creation, debugging, refactoring, documentation, and maintenance. Although LLM-assisted development can improve developer productivity, generated code cannot automatically be regarded as reliable or secure. In particular, nondeterministic behavior in software tests, unreliable validation procedures, inadequate historical testing information, and insufficient root-cause analysis can weaken confidence in AI-generated software. This paper proposes a robust framework for reliable and secure LLM-assisted software development by integrating LLM-based code generation with systematic validation, flaky-test detection, historical test analysis, static prediction, and automated root-cause assessment. The framework synthesizes findings from existing research on flaky-test detection and prediction and extends their implications to AI-assisted development environments. The methodology defines a multi-stage pipeline covering requirement interpretation, constrained code generation, static analysis, test generation, flakiness assessment, reliability-oriented test prioritization, security validation, and human-controlled release decisions. The analysis indicates that reliability should be treated as a continuous property rather than a final-stage testing outcome. The proposed framework also highlights the importance of separating genuine software defects from nondeterministic test failures, since unreliable tests can distort the evaluation of generated code. The paper contributes a conceptual architecture for combining trustworthy LLM-assisted development with established software-quality mechanisms and identifies directions for empirical validation, adaptive risk scoring, and enterprise-scale deployment.