Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation RAG, is a technology that enhances the capabilities of large language models (LLMs):
1. Retrieval: The system first searches through a knowledge base to find the most relevant information related to a query or topic.
2. Generation: It then uses this retrieved information to generate a coherent and context-specific response or content.
But LLMs have issues, right? Like hallucinations? That's why we went a step further and developed TaG-RAG, our hallucination-free framework that gives clinicians the accuracy, consistency and explainability needed for their jobs.
Why is TaG-RAG right for healthcare?
1
Quick
Doctors often need immediate answers. TaG-RAG swiftly retrieves pertinent information from vast medical databases, saving time compared to manual searches through multiple different documents.
2
Enhanced-Decision Making
By providing comprehensive and evidence-based information, TaG-RAG can support doctors in making informed clinical decisions. Any knowledge base can be transformed into a searchable database using RAG-TaG.
3
Safe and Accurate
Our TaG-RAG model can only access the knowledge made available to it. As such, if they don't know an answer, they can say so, reducing their propensity to hallucinate.
4
Explainable
Our TaG-RAG model is able to cite the information used to generate the response, hence are inherently explainable unlike large language models.
5
High Quality Input Data
Rubbish in = rubbish out with AI models. By using a high quality, critically appraised evidence base with TaG-RAG, clinicians can obtain high quality investigation, treatment and management information.
6
Reduced Cognitive Load
With an overload of medical data available, TaG-RAG filters and presents only the most relevant information, reducing the burden on doctors to sift through excessive data.