What Marketers Should Know about RAG Tech for AI

Read time: 3 minutes

Hi AI Pro!

There’s an alternative way for marketers to use LLMs like ChatGPT that reduces the possibility of relying on false or out-of-date information when analyzing data or generating content. It’s called “RAG” for short.


In a recent Wall Street Journal article, Gartner analyst Arun Chandrasekaran stated that around 80% of enterprises are currently using retrieval augmented generation (RAG) technology to customize AI models, in order to analyze data or produce content using existing large language models like ChatGPT.

In the same article, Edo Liberty, founder and CEO of Pinecone, a provider of GenAI applications, said the company’s combined use of vector search and RAG tech has contributed to its impressive growth from just a few hundred customers prior to the release of ChatGPT to over 5,000 last year.


LLMs like ChatGPT pull information from the internet to quickly generate content. However, this sometimes produces results that aren’t entirely accurate, leading to what's known as AI “hallucinations,” posing a significant problem for brands that prioritize accuracy and consistency.

RAG is a framework that integrates a reliable knowledge base from outside an LLM model’s training data to generate more accurate responses, using authoritative and up-to-date sources that can be trusted.

Unlike LLMs that rely solely on their training data, RAG systems leverage real-time content retrieval, expanding the information base they can access. RAG can generate marketing content at scale, faster, and more cost-effectively, sourcing data from a company's proprietary sources.


To effectively incorporate RAG technology, marketing professionals should:

Identify Key Areas:

Determine which aspects of your marketing strategy can benefit most from RAG, such as content creation, personalization, or customer service.

Integrate with Existing Tools:

Ensure that RAG systems are compatible with your existing marketing tools and platforms. This will streamline the implementation process and enhance overall efficiency.

Train Your Team:

Educate your marketing team about RAG technology and its benefits. Providing training will help them understand how to use RAG tools effectively and integrate them into their workflows.

Monitor and Optimize:

Continuously monitor the performance of RAG-driven initiatives. Gather feedback, analyze results, and make necessary adjustments to optimize the technology’s impact on your marketing efforts.

Bottom Line:

RAG represents a significant advancement in how businesses can leverage AI and LLMs to improve efficiency and decision-making. It’s still early days, but new features based on RAG are continuously rolling out.

The initial setup and ongoing maintenance of RAG systems can be costly. This includes expenses related to technology infrastructure, software development, and skilled personnel. High costs can be a significant barrier to entry for smaller businesses or startups.

Smaller organizations seeking to leverage RAG should explore cost-effective cloud-based solutions that reduce the need for on-premises infrastructure, or explore partnerships or collaborations with tech providers for shared-resource utilization.

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