Fine-Tuning vs RAG | Choosing the Right LLM for eCommerce

Updated 8 February 2024

Fine-tuning and RAG (Retrieval Augmented Generation) are the common techniques used for natural language processing. Here we are going to discuss which of the techniques is the right choice and choosing the right LLM for your eCommerce.

Fine Tuning

Fine Tuning involves taking a pre-trained model like GPT and training it for a specific data set or domain. 

There can be a scenario in your eCommerce where you want to give generic replies to your customers for certain products from the chatbot. In this case, you may want to fine-tune the model with your product data like description, customer reviews, or purchase histories. 

The chatbot will give tailored responses to eCommerce queries like product enquiries or customer support questions. 

RAG(Retrieval Augmented Generation)

RAG refers to a process of both retrieval and generation. It first retrieves information from a large database or knowledge base and then generates output based on the available information. 

Let’s say you are looking to purchase a watch from the eCommerce website. You ask the bot ” Please suggest some watch for men” The chatbot first checks the database matching the product criteria and then generates the response.

 

So you see using either Fine-Tuning or RAG depends on your specific need use cases and you can choose the right LLM for eCommerce. 

If you want the model to simply understand and generate responses based on product enquiries and provide customer support, you may use fine-tuning. But let’s say you have a large database and you want the model to first look into the database and then generate the response, you can make use of RAG.

BagistoGenAI is going to release a GPT model as part of its initiative to build Generative AI and LLM-based technologies for native support to the Bagisto eCommerce framework.

Thanks for reading this blog. Please comment below if you have any questions. Also, you can Hire Laravel Developers for your custom Laravel projects.

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