Part 3 - What can AI say?
Healthcare is a complex field with highly specialised terminology, making it essential for the Inavya team to develop a model that could generate natural, human-like conversations that users could easily understand. It was also crucial that the model could accurately process both the context and detail of user requests.
One of the most effective ways to enhance an LLM’s performance for sector-specific tasks—such as in healthcare—is through Retrieval-Augmented Generation (RAG).
What is Retrieval-Augmented Generation (RAG)?
RAG is a hybrid framework that combines the baseline generative capabilities of LLMs with a semantic similarity search function. This means that, instead of relying solely on the information contained in the model’s original training data, RAG dynamically retrieves relevant content from external knowledge sources to augment its responses.
Consider this analogy:
Before medical training, a doctor already knows how to speak and hold a conversation—this is equivalent to an LLM’s baseline generative capability.
Through medical education, the doctor builds a specialised body of knowledge. When consulting a patient, they rely on this medical expertise rather than drawing on random information from the internet—this is equivalent to the knowledge base that an LLM retrieves from.
A skilled doctor doesn’t simply recite textbook definitions but adapts their language, tone, and explanations to suit the patient’s understanding. Similarly, RAG allows an LLM to generate responses using conversational skills while incorporating precise, contextually relevant information from the knowledge base.
RAG is particularly well-suited for enterprise use cases like healthcare, where accuracy and relevance are critical. By integrating domain-specific knowledge bases, including potentially personal or private data (with appropriate safeguards), RAG-powered chatbots can understand and process complex and specialised queries, maintain high relevance within a specific sector, and provide context-aware, personalised response. This context-specificity and adaptability made RAG an ideal foundation for Inavya’s tool, ensuring it could meet the real-world needs identified during the assessment phase.