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While AI (artificial intelligence) has many uses and comes in many varieties, the average e-commerce marketer may have only heard about certain tools, such as ChatGPT. However, understanding the different types of AI algorithms is vital to growing your brand presence online.
A newer and important element of AI that every e-commerce marketer needs to understand is retrieval-augmented generation (RAG). What exactly is RAG, and how does it work?
Learn more below about the essentials of retrieval-augmented generation and how it works together with large language models (LLMs).
The Basics Of Retrieval-Augmented Generation
On the most basic level, RAG is an AI framework that assists with getting data from knowledge bases external to the LLM. RAG retrieves facts to create supplementary knowledge for large language models. This ensures the LLMs are as current and accurate as possible and also provide verifiable accountability for users into the response generation process of the LLM.
In other words: RAG is an AI framework for retrieving facts from an external knowledge base to ground large language models (LLMs) on the most accurate, up-to-date information and to give users insight into LLMs' generative process.
How Does RAG Work?
As AI models continue to grow in popularity and usefulness, many people want to learn more about the ins and outs of these models, including how they can be leveraged to assist businesses.
Let’s dig in with an analogy of how LLMs work. Say you work for a media company that wants their audience to use chat AI models to get answers about movies, celebrities, etc. While the LLM could easily answer any inquiries about the history of film or the background of celebrities, it couldn’t answer which celebrity won an award the night before.
This is because LLMs need to be retrained to get caught up on new information. This process is lengthy and expensive. While LLMs are impressive in how much information they can generate and understand, they are limited.
RAG, in this case, would improve your LLM by referencing your media company’s data set for accurate answers to more recent questions and prompts.
Simply put, RAG helps LLMs give better answers by creating a database of more dynamic data. It converts this information into a format that is easily accessed by the generative AI.
RAG in E-Commerce
EKOM leverages RAG as part of a broader AI/LLM approach in order to auto-generate digital assets for e-commerce clients. Using data from our clients’ data systems, as well as real-time search data from trusted sources like SEMrush, ahRefs, Google Search Console, and more, EKOM provides users with the most accurate and granular product information possible - all in their brand voice.
As an example, let’s say you require a product description page (PDP) for a sweatshirt that’s sold in several colors, and you want to have description copy written for a red version of that sweatshirt.
Your brand could upload a dataset – perhaps a .CSV file with all the various attributes of the product, including colors. Then, when the AI is instructed to write the PDP for this particular sweatshirt’s SKU, the LLM could use RAG to route the query through the .CSV file, learn that this particular sweatshirt is indeed sold in red, and can then generate accurate information about the red sweatshirt. This ensures the information is accurate and that the PDP does not contain a false AI hallucination about a non-existent red variation of the sweatshirt.
Important Points And Takeaways About RAG
As an overview, RAG is a relatively new AI framework that optimizes LLMs by building target information without changing the entire model of the LLM itself. It helps give more accurate, current information to users, especially in a particular industry or organization.
RAG data is kept in a “library” represented by numerals using specific algorithms inside the LLM. By storing the information in a vector database, users can get the answers they want faster.
To learn more about how EKOM uses RAG and other AI technology, schedule a walkthrough today.
