Code Generation with LLMs: Practical Challenges, Gotchas, and Nuances

Diving into the real-world highs and lows of using large language models to write code, from their uncanny ability to churn out snippets to the hidden traps — like outdated knowledge, security slip-ups, and integration woes — that demand careful navigation, illustrated with vivid examples and hands-on strategies to make AI a coder’s ally, not a liability.

The promise of code generation with large language models is tantalizing: faster development, automated boilerplate, and a helping hand for complex tasks. Yet, as I discovered recently while wrestling with a dated SDK issue — an intriguing challenge where an LLM confidently suggested a deprecated API call no longer supported in the latest release — the reality is far more nuanced.

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Here is a simple example. Azure .NET OpenAI v2 has breaking changes. In a nutshell OpenAIClient became AzureOpenAIClient but it seems no one told LLMs, and how could they.

Therefore if you try to do this simple prompt — which works perfectly fine for all python…