Both Grounding and RAG (Retrieval-Augmented Generation) play significant roles in enhancing LLMs capabilities and effectiveness and reducing hallucinations. In this post, I delve into the subtle differences between RAG and grounding, exploring their use in generative AI applications in healthcare. 

Grounding vs RAG in Healthcare Applications 
Image credit: [ Michael Havens -], CC BY 2.0, via Wikimedia Commons

What is RAG? 

RAG, short for Retrieval-Augmented Generation, represents a paradigm shift in the field of generative AI. It combines the power of retrieval-based models with the fluency and creativity of generative models, resulting in a versatile approach to natural language processing tasks.  

  • RAG has two components; the first focuses on retrieving relevant information and the other on generating textual outputs based on the retrieved context. 
  • By incorporating a retrieval mechanism into the generation process, RAG can enhance the model’s ability to access external knowledge sources and incorporate them seamlessly into its responses. 
  • RAG models excel in tasks that require a balance of factual accuracy and linguistic fluency, such as question-answering, summarization, decision support and dialogue generation. 

Understanding Grounding 

On the other hand, grounding in the context of AI refers to the process of connecting language to its real-world referents or grounding sources in perception, action, or interaction. 

  • It helps models establish connections between words, phrases, and concepts in the text and their corresponding real-world entities or experiences. 
  • Through grounding, AI systems can learn to associate abstract concepts with concrete objects, actions, or situations, enabling more effective communication and interaction with humans. 
  • It serves as a bridge between language and perception, enabling AI models to interpret and generate language in a contextually appropriate manner. 

Contrasting RAG and Grounding 

While RAG and grounding both contribute to enhancing AI models’ performance and capabilities, they operate at different levels and serve distinct purposes in the generative AI landscape. 

  • RAG focuses on improving the generation process by incorporating a retrieval mechanism for accessing external knowledge sources and enhancing the model’s output fluency. 
  • Grounding, on the other hand, emphasizes connecting language to real-world referents, ensuring that AI systems can understand and generate language in a contextually meaningful way. 
  • In general, grounding uses a simple and faster model with a lower temperature setting, while RAG uses more “knowledgeable” models at higher temperatures. 
  • Grounding can also be achieved by finetuning a model on the grounding sources. 

Implications for Healthcare applications 

In the domain of healthcare, grounding is especially useful when the primary intent is to retrieve information for the clinician at the point of patient care. Typically, generation is based on patient information or policy information and the emphasis is on generating content that does not deviate much from the grounding sources. The variation from the source can be quantitatively measured and monitored easily. 

In contrast, RAG is useful in situations where LLMs are actively involved in interpreting the information provided to them in the prompt and making inferences, decisions or recommendations based on the knowledge originally captured in the model itself. The expectation is not to base the output on the input, but to use the provided information for intelligent and useful interpretations. It is difficult to quantitatively assess and monitor RAG and some form of qualitative assessment is often needed. 

In conclusion, RAG and grounding represent essential components in the advancement of generative AI and LLMs. By understanding the nuances of these concepts and their implications for healthcare, researchers and practitioners can harness their potential to create more intelligent and contextually aware applications.