Why and How Retrieval-Augmented Generation Improves GenAI Outcomes

Unstructured

LLM

This research paper from BARC defines retrieval-augmented generation, or RAG, a type of workflow that prompts GenAI language models with domain-specific data to improve their accuracy. It evaluates three architectural approaches – vector RAG for semantics, relational RAG for database values, and graph RAG for ontologies – as well as hybrid solutions that combine all three approaches.

Summary

Retrieval-augmented generation (RAG) has emerged as a promising solution to address the accuracy and governance challenges faced by generative AI (GenAI) language models. This research note explores RAG as a workflow that enhances GenAI outputs by incorporating domain-specific data into the prompting process. RAG reduces the risk of hallucinations and improves the trustworthiness of AI-generated responses, making it a crucial tool for businesses looking to leverage GenAI technologies responsibly.

The report outlines three primary architectural approaches to RAG:

vector RAG for semantic understanding of unstructured data
relational RAG for retrieving accurate values from databases, and
graph RAG for interpreting complex relationships in graph databases.

These approaches can be used individually or combined to address diverse data types and use cases.

The report concludes with guiding principles for successful RAG implementation including evaluating approaches based on complexity, embracing hybrid solutions, and considering integrated platforms to simplify implementation and reduce operational risk.

Key learnings:

The three main architectural approaches to RAG: vector, relational, and graph, and their respective use cases.
The evolution of data analytics
Challenges and benefits associated with implementing RAG solutions
Guiding principles for successful RAG implementation