AI sometimes gives answers that look smart but are completely wrong. These errors are called hallucinations. Grounding is the fix—it connects AI to real-world data to keep responses accurate. So, what are grounding and hallucinations in AI exactly?

Grounding uses methods like fine-tuning and retrieval-augmented generation (RAG). Fine-tuning aligns outputs with verified sources, while RAG retrieves external data in real time, making answers more reliable.

Hallucinations occur from poor training data, overfitting, vague prompts, or AI’s lack of common sense. The result is content that seems logical but isn’t factual.

Why It Matters: Grounding cuts hallucinations by ensuring outputs are backed by facts. This is crucial in banking (chatbots giving precise account info) and healthcare (avoiding false diagnoses).

Prevention Tips: fine-tune with domain data, use feedback, apply RAG tools, and write clear prompts.

In short, grounding is the safety net that prevents hallucinations, making AI more accurate and trustworthy.
View details here: What is Grounding and Hallucinations in AI? Tips To Prevent Hallucinations