This thesis presents TIGER, a test-driven and documentation-guided framework designed to improve the functional reliability of Python code generated by Large Language Models (LLMs) in realistic software development settings. Unlike conventional approaches that often expose models to ground-truth implementations or rely on synthetic benchmarks, TIGER operates in a constraint-rich environment that better simulates the experience of human developers: the model is given access only to high-level natural language artifacts—such as README files, reStructuredText documentation, and function docstrings—and is guided solely by the results of automated test execution. The LLM is never allowed to see any reference implementation of the target functions.