Imagine an AI trying to write code, but it keeps making up imaginary functions – like a chef following a recipe but substituting fantasy ingredients. This is the problem of 'hallucinations' in large language models (LLMs) for code generation. These LLMs, trained on massive amounts of code, can sometimes generate incorrect or nonsensical API calls, especially with newer or less common APIs. Researchers explored this issue and found a clever way to mitigate these coding nightmares by grounding AI in reality, and presented a new benchmark to measure API hallucination called CloudAPIBench. Just as human developers rely on documentation when unsure about API usage, researchers gave the AI access to relevant API documentation using technique called Documentation Augmented Generation (DAG). This helps improve the AI’s accuracy, but it can sometimes backfire by distracting the model with unnecessary information. The blog post discusses how fine-tuning API access through clever retrieval methods helps to balance the need for documentation with the AI’s own internal knowledge, leading to more reliable and efficient code generation, hence taming the hallucinations effectively.