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What is Grounding in AI?

Grounding in AI helps in reducing the hallucinations by associating the LLM's responses to real-time enterprise data, acting as a fact-checker for the LLM.

What are Hallucinations in AI?

Hallucinations in AI are false results or responses produced by the AI that can be caused by many reasons including: Unreliable training data Overtraining during learning Confusing prompts Creativity overload in AI

How Grounding in AI Can Reduce AI Hallucinations?

Prompt Engineering: By feeding LLMs with highly specific prompts derived from real-time data, grounding ensures that the AI generates relevant and accurate responses. Fine-tuning: Grounding involves fine-tuning LLMs using specific use case data, ensuring the generated responses are fact-checked and accurate.

Data Fusion: Integrating data from various enterprise systems guarantees the utilization of comprehensive and accurate information, reducing the probability of hallucinations. Connecting to Real Data: Grounding makes sure that the AI's outputs rely on current, factual data rather than solely its initial training, ensuring the delivery of accurate responses.

Improving Data Quality: Grounding by incorporating high-quality, verified data from enterprise systems eliminates errors and misinformation that might be present in the training data.

Using Retrieval-Augmented Generation (RAG): This framework improves LLM outputs by fetching pertinent data from structured and unstructured sources, thereby preventing hallucinations. Memory Augmentation: Storing external knowledge for convenient access ensures the LLM can retrieve accurate data while generating responses.

AI can sometimes make up stuff, like a story machine with a loose screw. Grounding fixes this by checking the AI's answers with real information, making its responses more trustworthy and accurate.