Confabulation/Delusion/Hallucination:
How RAG Can Help Reduce Hallucination
Understanding RAG’s Impact on Mitigating Artificial Intelligence Hallucination Risks
Hallucinations in AI are a common problem that can lead to inaccurate and unreliable results. RAG is a technique that can help reduce hallucinations by providing the model with relevant context and information to improve the accuracy of its responses. Ensuring the accuracy and reliability of generated content is a significant challenge in the advancing field of artificial intelligence. One common issue faced by AI models, particularly in natural language processing (NLP), is the tendency to produce hallucinations—responses that are either factually incorrect or nonsensical.
Text generation technologies have advanced significantly, offering a wide range of applications from content creation to advanced AI systems. However, a critical issue these systems often encounter is the generation of inaccurate or misleading text, commonly referred to as hallucinations. This blog post explores how Retrieval-Augmented Generation (RAG) can be a valuable tool in addressing this challenge.
