- RAG integrates external data for enriched responses, while LLM Fine-Tuning adjusts pre-trained models for domain-specific accuracy
- Both of them fall under the wider umbrella of AI approaches geared to improve the models’ understanding of complex user requests in a given domain
RAG with Fine-Tuning
Consider the case of a Gen-AI Virtual Assistant trained to answer questions related to the healthcare domain. Let’s consider the following user question as an example: “What’s the effect of drug X on my hypertension and diabetes?”
An LLM fine-tuned with medical knowledge understands that “hypertension” and “diabetes” are chronic diseases, and “drug X” is a medicinal variable that can have an impact on these. The fine-tuned large language model can provide information like “drug X is commonly prescribed for condition Y, which is unrelated to hypertension and diabetes.” This specificity of contextual perception demonstrates the advantage of a fine-tuned LLM.
However, the user query also implicitly seeks information about potential interactions between drug X and their existing hypertensive and diabetic medications, a complex question that demands extensive retrieval of healthcare data. Even with the best LLM strategy, this task can exceed a fine-tuned LLM’s capacity, underscoring a limitation of this model.
The RAG approach leverages a vast database of healthcare information that includes details regarding drug interactions. For the same user query, the RAG might extract relevant information about “drug X”’s pharmacodynamics, its potential effect on diabetes and hypertension, and any known interactions with other medicines.
However, the RAG model could potentially face limitations when the query involves specialized terms or abbreviations common in healthcare. If “drug X” was a specific medical term unfamiliar to the system, the answer generation might be compromised, showing a limitation in its ability to decode highly specific medical abbreviations.
By marrying the benefits of improving LLM performance with fine-tuned LLM and RAG, a holistic approach emerges that could address the user query more comprehensively. The fine-tuned LLM component recognizes “drug X”, hypertension, and diabetes plus their related terminologies or abbreviations and anchors the context accordingly.
Meanwhile, the RAG model retrieves relevant information about “drug X”, its effects on hypertension and diabetes, and any contraindications for patients with these medical conditions, providing a detailed, nuanced response to the user’s request.
Combining both models effectively navigates the sophisticated interplay between specialized medical vocabulary and the need for in-depth healthcare information recall. This enhanced UX better addresses the complexity and unique requirements of healthcare domain inquiries.