This research comprehensively surveys how Large Language Models transform code generation, enabling users of all technical backgrounds to create executable software through natural language instructions.

• Identifies key challenges and limitations in LLM-based code generation
• Examines specialized fine-tuning techniques that enhance performance
• Reviews evaluation methodologies for measuring code generation quality
• Explores practical applications across industries

For engineering teams, this research provides critical insights into leveraging LLMs as productivity multipliers, democratizing software development, and understanding the current boundaries of automated programming assistance.

Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications