Retrieval augmented generation or RAG combines pre-trained parametric models and non-parametric memory retrieval, to provide higher quality insights based on entered queries. The AI architecture solution allows users to interact with generative systems to acquire greater accuracy and lower risk of information leak.
RAG allows enterprises to develop highly technical applications and systems for better experiences. RAG can help stakeholders achieve their query-driven tasks with higher accuracy. By leveraging more updated information, both technical and insight based, RAG can enable better research, standardization, quality testing, customer interactions, etc.
RAG can be used across enterprises to help make generative AI more user-focused. By going beyond the standard training data, and storing information as vector databases, it can improve the effectiveness of knowledge intensive tasks. More relevant information can be generated about production insights, solutions, technical features, etc. in many cases.
What is Retrieval Augmented Generation (RAG) in GenAI?
Essentially, a retrieval augmented generation technique is a design based approach or a complete architectural approach for generative AI. It allows the combination of information retrieval approaches with language models or LLMs to improve the quality of the generated information.
This strategy or technology allows for the most updated information to be shared to the user, who may want to perform enterprise queries on large language models. The RAG AI framework depends on two distinct and intertwined parts, namely the retriever (extractor) and the generator.
The extractor component of RAG searches updated data bases for information that is relevant to the query of the user. It can go beyond standard language learning models and find sources of information within larger data bases. The generator makes sense of the information extracted and provides a response to the user.
Why use Retrieval Augmented Generation?
A retrieval augmented generation system can leverage wider networks of information for producing and answer to a query. By storing information in a vector database, it can pull information from more relevant sources when compared to a standard response bot.
More up to date information can be generated in most cases when using RAG for GenAI. While most open source systems, such as Google and Facebook AI research tools, rely on non-updated data, RAG updates the information capturing capabilities of GenAI.
It's also important to note that for highly knowledge-driven tasks, a more task-specific architecture may be viable long-term. You can get better results when using an architecture that is specifically designed for specialized tasks within the enterprise, developed by the technical experts for the enterprise.
Key enterprise use cases for RAG in GenAI
There are several important use cases for RAG and GenAI, which is why it is important to consult with the professionals. You can get insights that are highly accurate and precise to the customers' queries, which can help improve relationship building, sales, lead management, research, analytics, etc.
Let's explore some of the major use cases where RAG and GenAI can be applied.
Training
Training for employees, across levels, can be done through RAG and GenAI. New data generated using the query and retrieval model, can also be used to answer subsequent training questions. AI models can also be used to track information acquisition, retention, ability to utilize information, etc.
Patient insights
A patient data search engine based on insight generated and retrieve data models, can be used for patient insights for pre-op care, treatment protocols, personalized care, etc. More accurate information can be generated when handling hundreds of thousands of patient data points.
Customer service
Using semantic search and understanding query context through large language model updating, the system can answer questions based on external data sources as well as internal. Your customer service modes can use non-sensitive relevant documents as well as LLM training data to provide more accurate information.
Assistants or interaction bots
Your customers, employees, and other stakeholders can interact with your product, solution, or enterprise, for specific queries that can be solved using RAG in GenAI. The assistant can pull data from external knowledge sources to make such knowledge intensive tasks more updated.
Legal research
The information retrieval component of RAG can help with legal research, especially when searching for highly specific information present across different cases. Through keyword search and natural language processing, more accurate legal information can be generated based on prior cases.
Understanding the benefits of RAG in GenAI
Enterprises can use generative AI and RAG models to lower the risk of inaccuracy, and improve the protection of sensitive information. The merits of adopting the right type of RAG solution are significant, especially when there are larger numbers of queries performed on enterprise systems for research, knowledge acquisition, etc.
Reduced need for updating
Through the introduction of RAG into the system, there is a lower need for constant updating and ensuring that the information is latest as per the query request. RAG ensures that the generative AI model pulls data from the most updated data bases.
Lowering risk of misinformation
There is a lower risk of hallucinations or incorrect information being shared upon a query being introduced when there is a RAG AI embedded. Misinformation can be curtailed at the enterprise level, when there is a comprehensive RAG system introduced.
Low risk of sensitive data sharing
There is also a lower risk of sensitive IP or data being shared with users when there is a query introduced. By allowing for retrieval augmented generation to be leveraged, there is a lower risk of information being used by accident which may include sensitive information as well.
Improving accuracy of insight shared
When an employee or a potential customer enters a highly specific query, with regards to the product specifications or technical details, the response of the generative AI model should be highly precise. This can be achieved with greater accuracy using a RAG architecture.
Conclusion
Generative AI can be made that much more powerful and interactive with the help of retrieval augmented generation. RAG can empower enterprises to develop more powerful applications that can focus on solving complex challenges in stakeholder interactions, research, and data analysis.
Not only can enterprises create new solutions, they can also empower their existing systems to become more generative and updated. RAG can be implemented in a range of systems to be made that much more interactive and solutions-oriented for customers.