What is Retrieval Augmented Generation (RAG)?

For all the buzz and business potential around generative AI, this relatively new form of AI comes with some serious caveats. While the large language models (LLMs) that power generative AI can create new and original content for business users, the technology is prone to spit out erroneous, outdated or even totally made up “hallucination” results.

That’s where Retrieval Augmented Generation, or RAG, comes in. Let’s take a look at how our ECI Large Language Applications (ELLA) brings the benefits of RAG to our financial sector clients. 

RAG Addresses the Drawbacks of Generative AI

Especially since the November 2022 launch of ChatGPT, generative AI has transformed the enterprise landscape, but the technology is far from perfect. Generative AI datasets are often out of date and lack specific domain context, meaning results are typically “frozen in time” and too general to be of use for specific financial sector processes or functions. As mentioned above, results can also be inaccurate; and there’s often no traceability in determining how the LLM derived its outputs.

These shortcomings can overshadow the benefits, introducing error and risk into the organization. For instance, poorly vetted generative AI results can spit out flawed analysis, market evaluations or bespoke recommendations that can cost a financial firm time, money and potential security or regulatory breakdowns. Fortunately, RAG was designed to solve the shortcomings of “out of the box” LLMs that are of little use for enterprise-grade, context-dependent tasks such as generating earnings reports or conducting due diligence on portfolio companies.

RAG remedies these issues by merging retrieval-based processes involving company and domain-specific data, with the generative functions of ChatGPT or similar LLM platforms. More specifically the RAG process retrieves relevant chunks of documents from a corpus of verified company or industry data and then uses this context to generate informed and nuanced responses from the LLM. 

ECI is Putting RAG into Action with the ELLA

The importance of RAG figured prominently in ECI’s recent AI forum at Microsoft’s New York Tech Center in Times Square, with ECI’s AI product specialist Clay Karges calling RAG “probably the single most powerful application of generative AI within the enterprise.” He was talking in the context of a live demo for the audience of the ECI Large Language Application (ELLA), which includes a RAG component. 

ELLA puts RAG into action by leveraging proprietary documents as the foundation for personalized insights and precise, context-aware answers and recommendations. This helps optimize results by ensuring that the LLM is referencing an authoritative knowledge base of company or verified external data sources, not just the LLM’s training data, before generating a response. 

The result is the time-saving functionality of generative AI, paired with a level of accuracy and relevance in results necessary to ensure the integrity of business operations and stay compliant. ELLA facilitates this with a highly protected and private environment for access, use and creation of generative results, with real-time visibility and specific controls over the inputs, outputs and usage of AI. This helps clarify the provenance of results, so clients can understand the logic behind specific generative AI outputs. 

Throughout, ELLA is purpose built for alternative investment organizations, ensuring that ECI’s domain mastery, including up to date business and regulatory context, is infused in the AI results generated for hedge funds, private equity firms, banking institutions and other financial service firms that must operate amid strict security and compliance requirements. Learn more about how ELLA brings confidence and security to generative AI implementations that more and more financial sector firms are embracing today.