Smart Shopify Shopping Lists: A Comparative Analysis of MCP vs. RAG Methods

MCP and RAG, two core technologies for creating shopping lists on Shopify by analyzing user text, operate in fundamentally different ways. Understanding their differences is the key to creating a more intelligent e-commerce experience.

The MCP (Maximum Common Subsequence) Method: Keyword Matching-Focused

MCP is a keyword search method that directly finds and matches the text a user enters in product names, descriptions, and other fields. As an example of how it works, when you search for "red t-shirt," it finds products that contain both the words "red" and "t-shirt" in their product information. The primary advantage of this method is that it is efficient and simple to implement for searches with clear keywords. However, it has several limitations. It exhibits poor intent understanding, struggling to respond to vague queries like "birthday gift recommendations." It also has synonym issues, as it cannot recognize "sneakers" and "athletic shoes" as the same thing. Furthermore, it suffers from a lack of context because it does not consider user history or preferences. Finally, it has low recall, meaning it can miss relevant products if the keywords do not match exactly.

The RAG (Retrieval-Augmented Generation) Method: Based on Semantic Understanding and AI Generation

RAG is an advanced method that combines semantic search with the generative power of Large Language Models (LLMs), allowing it to understand the user's search intent rather than just matching keywords. Its process involves three main steps. First, through vector embeddings, all product information is converted into numerical vectors to store semantic relationships in a database. Second, using semantic search, the user's query is also converted into a vector to find the most semantically similar products; for instance, a search for "warm jacket" can find a "wool jacket." Third, the retrieved product information is passed to an LLM to generate LLM-augmented results, creating a rich shopping list that includes reasons for recommendation or comparisons, not just a simple list of items. This approach has many advantages, including high intent understanding for complex and natural user queries, the ability to create a personalized shopping experience by reflecting user history, the generation of rich information like product summaries and comparisons, and high recall that excels at finding relevant products even with ambiguous queries. The main considerations for this method are that its implementation is complex and can incur costs related to using LLM APIs.

Conclusion: MCP vs. RAG

In summary, MCP is suitable for simple, direct searches, while RAG provides a personalized and intelligent shopping experience by deeply understanding user intent. Therefore, RAG can be considered the future technology for enhancing user engagement and recommendation accuracy in modern e-commerce.