Explain fine-tuning in large language models.

In the context of Generative AI, a “hallucination” refers to the generation of information that is not accurate, factual, or grounded in real data. These hallucinations occur when AI models, particularly large language models (LLMs), produce outputs that sound plausible but are actually incorrect or fabricated. This can happen in tasks such as text summarization, translation, code generation, or even answering questions.

Hallucinations are not caused by bugs in the software but are a byproduct of how generative models are trained. These models learn patterns from vast datasets found on the internet, including books, articles, and websites. However, they do not have a built-in fact-checking mechanism or access to real-time data unless explicitly integrated. As a result, when prompted to provide information outside of their training data or when the prompt is vague or ambiguous, they may “hallucinate” answers by predicting the most statistically likely continuation of the input—even if it’s untrue.

For example, if you ask a Gen AI model, “Who won the 2025 Nobel Peace Prize?” before that data is available, it might still give a very confident but entirely fictional answer. In high-stakes applications like healthcare, law, or finance, such hallucinations can be dangerous and misleading.

To reduce hallucinations, techniques like reinforcement learning from human feedback (RLHF), retrieval-augmented generation (RAG), and grounding models in verified datasets are being actively researched. Nevertheless, hallucinations remain one of the key limitations of current generative models.

Understanding and addressing hallucinations is critical for developing trustworthy AI systems. For professionals looking to explore this field deeper, pursuing a Gen AI and machine learning certification can provide the necessary foundation and skills.

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