Why RAG is the best way to put GenAI to use for market and competitive intelligence research

Northern Light has embraced retrieval-augmented generation (RAG) from the first day we launched our generative AI (GenAI) capability within SinglePoint™, Northern Light’s market and competitive intelligence knowledge management platform, more than 18 months ago.  We have been talking about RAG’s advantages since then and have received an enthusiastic reception as we’ve demonstrated the approach to dozens of clients and prospects, as well as numerous attendees at industry conferences.

As we wrote in a blog post back in May, “There is growing recognition across the business world that the technique known as RAG is foundational to the effective use of GenAI within enterprise applications.”

But just what exactly is RAG?  Consulting firm McKinsey explains it this way:

“Retrieval-augmented generation, or RAG, is a process applied to [large language models, or LLMs] to make their outputs more relevant in specific contexts. RAG allows LLMs to access and reference information outside the LLMs own training data, such as an organization’s specific knowledge base, before generating a response—and, crucially, with citations included. This capability enables LLMs to produce highly specific outputs without extensive fine-tuning or training, delivering some of the benefits of a custom LLM at considerably less expense.”

Northern Light has found this to be true for mining market and competitive intelligence research content within an enterprise knowledge management system.  Our clients who have activated GenAI within their SinglePoint platforms report their GenAI search summaries are highly accurate and save hours of professional staff time.

In its RAG primer, McKinsey uses the analogy of a librarian stocking the shelves of a library and creating a thorough index of the books; then:

“Whenever a user asks a question on a specific topic, the librarian uses the index to locate the most relevant books.  The selected books are then scanned for relevant content, which is carefully extracted and synthesized into a concise output.  The original question informs the initial research and selection process, guiding the librarian to present only the most pertinent and accurate information in response.  This process might involve summarizing key points from multiple sources, quoting authoritative texts, or even generating new content based on the insights that can be gleaned from the library’s resources.”

The key to RAG’s effectiveness is the quality of the content that the GenAI application draws upon.  As we noted in our blog post earlier this year, “Northern Light has mastered the art of aggregating reliable, vetted sources of business research for market and competitive intelligence, which is the domain of SinglePoint platforms.  Applying RAG to those content collections, GenAI in SinglePoint is a powerful tool to jump-start the research process.”

Addressing the broad opportunity for RAG in the enterprise, McKinsey concludes its RAG primer by stating, “LLMs enhanced with retrieval-augmented generation can harness the strengths of both humans and machines, enabling users to tap into vast knowledge sources and generate more accurate and relevant responses.”