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Integrating Large language models (LLMs) within modern software engineering workflows offers significant opportunities to accelerate the production of source code and make developer workflows more efficient. Realization of this potential is, however frequently obstructed by systemic impediments in the form of insecure and buggy code generation, inefficient resource use because of monolithic reasoning mechanisms and contradictory decline in developer productivity. This report introduces a new, multi-step prompt engineering framework that aims to overcome this limitation and fulfil the vision of fast, high-quality code generation. The framework’s key contribution is an Uncertainty -Guided Code First chain-of-thought (CoT) strategy that dynamically engages in sophisticated reasoning only when it is required, thus saving computational resources and enhancing accuracy. By combining this adaptive mechanism with a self-correction loop, the system comprehensively tackles common LLM-specific weaknesses including bug propagation and the appearance of non-prompted features. Simulation experimental analysis shows that this adaptive strategy extensively improves code quality and efficiency on common benchmarks, proposing a new, more efficient paradigm for human-AI collaboration in software development. The result demonstrate that a principled dynamic prompting strategy is vital to taking LLM- assisted development from a position of untapped potential to real-world and scalable effectiveness