Role of RAG (retrieval augmented generation) in e-commerce 

Here we’ll discuss the role of Retrieval Augmented Generation (RAG) in eCommerce and how it’s transforming search, recommendations, and customer experiences.

RAG-based AI system plays an important role in e-commerce nowadays to stay ahead in the game of advancement in technology. 

There are two methods to improve the LLM models, One is the RAG based the other is Fine Tuning. 

What is RAG? 

Retrieval augmented generation is a method to improve your LLM Models. RAG models help the LLM’s to provide more accurate answers to the customers for generative AI chatbots.

You can say private case training. 

It helps the LLM to retrieve the most updated information. The customer is grounded in the answer to something which is more believable less chance of hallucination. 

Say For example: You are having an event company and you ask your LLM to write about the latest event of your company. 

LLM has basic knowledge about all the topics, But if you want in respect to your event company it will get confused.

It gives AI hallucinations which is the inaccurate and misleading result of an AI model in AI powered personalization.

So to avoid that you need to train the LLM with the data set based on your event company. 

The main benefits of RAG is to help the AI model to provide accurate results. It provides a dynamic database to the LLMs and easy for new information updates.

In this case, you don’t need to train the LLM model with new data again. 

Where in Fine-tuning, you can fine-tune your LLM means you can take a pre-trained LLM say GPT and train at least one model parameter with the particular use case. 

Flow Diagram of RAG

There are many applications based on RAG from which chatbots have immense potential. Let’s discuss the same below:

i) AI Chatbots:

Smart Chatbots work as a virtual assistant to a customer. They provide answers in such a way that humans can easily understand. 

It provides a human-like interaction with the customers. Also nowadays chatbots are becoming multilingual as well. 

Siri, Alexa and Google Assistant are one of the examples of AI Chatbots. 

You can check the example below: I have created a Chatbot in Magento 2 for my e-commerce website, Now see how accurately it provides the answers.

You can check the video here:

ii) NLP to SQL:

NLP (Natural Language Processing) helps to convert the natural language to structured SQL queries. It is quick to retrieve information or Insights from the database. 

It can be used for confidential and more accurate data. For example in report generation, CRM, HRM etc 

For example: I have asked my eCommerce website system to show me the last 5 orders. 

So a person does not rely on technical expediting, It gives the data in a more user-friendly process. 

Or show me the orders for 2023. 

You can also check the video here:

iii) Semantic Search:

It is a technology which helps in understanding the meaning of the query asked by the customer and provides a meaningful answer. 

It helps Machine learning and Artificial intelligence to provide the semantic meaning answer concerning the search intent. 

For example: 

You can ask the chatbot to show “Red shirt for men in XL Size” it will show the shirts available in red-colour XL-Size. 

Let’s do an image search now, Let me enter a t-shirt it will show similar results available. 

iv) Invoice extractor:

This is also an example of a RAG-based system, for example, you have a big organization.

And you are getting multiple bills on a monthly basis like laptops, food, electricity, water etc, and that too in multiple formats. 

Now to add all those bills manually to your erp system will be hard work.

So just to optimize this work you can use an invoice extractor system which will help you to summarize the invoices in JSON format and upload them directly to the respective system. 

HTML format: 

You can check the video here:

This is all about the basic idea of how effectively you can use RAG in e-commerce.

Thank you for reading this documentation. For any queries or doubts, reach out to us at [email protected]. You can also raise a ticket at our HelpDesk System.