How RAG Adds Context & Accuracy to E-Commerce SEO

November 10, 2023

Today’s powerful AIs and large language models (LLMs) are built on “foundation models.” A foundation model is a massive pre-trained model that is specifically tuned to perform specific tasks. These foundation models are customized via prompt engineering (shaping inputs so that the model will produce the desired response).

Fine-tuning a foundation model can allow for the creation of a custom AI that is specifically trained on your business. That means your product names, brand language, specific tasks, and relevant contextual clues can all be brought into an LLM that is uniquely yours.

In many cases, this training happens offline. As a result, data that is created post-training is not native to the foundation model. So, how can your specially trained foundation model access post-training data and remain relevant?

Retrieval-Augmented Generation in E-Commerce

That’s where retrieval-augmented generation (RAG) comes in. RAG allows the foundation model to integrate external data in context. Retrieval-augmented generation adds context, accuracy, and relevance to your AI. Best of all, query responses have verifiable sources, as the users are allowed access to the model’s sources.

  • Without RAG: a user inputs a prompt into an LLM. The LLM generates a response based on the data it was trained on. The response might be correct. But it also may be outdated – and neither the LLM nor the user is aware of this problem.
  • With RAG: a user inputs a prompt into an LLM. The LLM first searches for relevant information in its knowledge sources (data sets, documents, APIs, etc.). It extracts relevant information from the knowledge sources and uses those to provide enhanced context in a generated text response. Equally as valuable, the LLM knows when it doesn’t know the answer, and can also inform the user of its limitations.

EKOM: Not Just an AI-Wrapper

Unlike many AI-related companies in the SEO space, EKOM is not just an AI-wrapper (meaning a service that integrates various AI tools into one application). Rather, EKOM brings to e-commerce companies proprietary data models that set the industry standard, allowing heads of SEO and e-commerce to automate the generation of digital assets, such as those seen on product description pages (PDPs). 

Training data remains a massive moat, and EKOM has the product data that’s needed for e-commerce stores to generate high-quality (and RAG-powered) digital assets. And as EKOM grows, so does the quality of your LLM.

To learn more about how EKOM helps e-commerce brands create original search-optimized content that performs at scale, schedule a walkthrough today.

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