Code generation is one of the most active areas of application of Large Language Models (LLMs). LLMs lower barriers to writing code and accelerate development process, so much so there are now research projects and businesses powered largely by LLM code generation process. It is however important to realise that the overall quality of generated programs depends on the quality of given prompts. Functionality and quality of generated code can be sensitive to user's background and familiarity with software engineering.
In this talk we will discuss several aspects of LLM code generation. We will talk about end-user software engineering paradigm and how it is put to practice today. We will investigate how sensitive this approach can be to changes in the requirements. And we will see how LLMs can be deceptively good at solving seemingly impossible problems with code.
