With today’s standards in the e-commerce space, shoppers expect to get fast, accurate, and personalized experiences across every touchpoint. Traditional AI tools and keyword-based search engines can’t fully satisfy such demands. That’s when retrieval-augmented generation (RAG) comes into play.
The global RAG market is expected to grow rapidly in the near future. In 2024, its size was $1.3 billion. By 2034, it is projected to achieve the mark of $74.5 billion, which reflects a CAGR of 49.9%. This demonstrates a huge demand for RAG tools across the markets.
In our article, we will focus on how RAG is transforming e-commerce operations and what makes it different from traditional LLMs.
Retrieval Augmented Generation: What It Is and How It Works
Let us start with a basic definition of this technology. It combines the retrieval of information from relevant sources with producing responses in natural language.
This approach helps overcome the limitations of LLMs. It delivers contextually relevant data from an external knowledge base, which improves factual accuracy.
The simplified RAG flow looks as follows.
- Client’s query. The user asks a question. For example, it can be “What’s the difference between the iPhone 15 and iPhone 16?”. This question is received by the framework.
- Semantic search in a vector database. The query is transformed into a dense vector using an embedding model. The system turns to a vector database (including product description, tech spec sheets, comparison tables, etc.) or a hybrid search engine to search for relevant documents or information.
- Retrieval and LLM prompting. The most relevant contextual documents are retrieved. In our case, the system can work with Apple’s official comparison chart, technical specification pages, and a product comparison blog post from the retailer. Then, this data is combined with the original query. This prompt is sent to the LLM.
- LLM response and post-processing. The LLM generates a response based on the retrieved information. The framework may apply post-processing before sending the final response back to the client.
The combination of RAG and an LLM for ecommerce is highly effective. RAG greatly extends the possibilities of LLMs with a retrieval step. Traditional large language models generate answers based only on the knowledge they learned during training. They don’t work with real-time or external data. But RAG can do it. As a result, LLMs can use the most relevant data to provide factually grounded and context-aware responses.
Regardless of the type of AI technology that you plan to deploy, you need to make sure that your business is ready for that. In this context, it’s vital to take into account not only financial but also infrastructural and cultural parameters. For example, on our blog, you can read about how to evaluate your business maturity for LLM integration.
In the table below, you can find the key differences between using an LLM alone and together with RAG.
Key Benefits of Retrieval Augmented Generation in E-Commerce
The implementation of RAG systems can be more expensive and challenging from a technical perspective than the use of an LLM. Nevertheless, all the efforts and investments will definitely pay off. Here is why.
Up-to-date Answers in Real Time
The e-commerce domain is highly dynamic. Such aspects as prices, availability, or product attributes can change frequently. Traditional LLMs are trained on static datasets. Given this, their responses become outdated very quickly. RAG addresses this by combining data retrieval from language generation, which makes it a powerful component of modern genAI development.
Great Scalability without Manual Updates
If you use only an LLM and want to maintain up-to-date responses, frequent fine-tuning is a must. However, it is a very costly and time-consuming process. With RAG, the knowledge base will be decoupled from the large language model. That’s why you won’t need to update the model itself each time you launch a new product line or when prices are changed. What you will need to do is update only the content source.
Higher Personalization
Personalization in e-commerce has evolved from a trend to a necessity.It’s already a standard. Customers expect to get experiences that will be tailored to their needs. LLMs alone don’t have access to such user-specific data. But RAG addresses this challenge. Dynamic context can be part of the retrieval process.
High-quality, Omnichannel Customer Support
Automation of customer support processes can help businesses significantly reduce their operational costs. Moreover, chatbots and virtual assistants are useful for optimizing the workload for human agents. But when new support channels and tools are added, it becomes more challenging to maintain consistency.
In such a situation, it is crucial to verify whether all channels rely on the same sources. RAG can ensure that. By centralizing knowledge retrieval from a single dataset, this approach minimizes errors and inconsistencies.
Smart Search and Product Discovery
It’s not uncommon when a person wants to find a product without knowing its name. Instead of just providing the exact model, people may use rather vague descriptions like “a smartphone with a good camera under $1000”. Traditional search engines are not good at interpreting such queries.
At the same time, RAG relies on semantic understanding to surface the most relevant content.
RAG in E-Commerce: Specific Use Cases
The rise of AI for eCommerce continues to accelerate. For example, today, AI-driven tools are implemented for dynamic pricing or for generating images and descriptions.
Now, let’s take a closer look at the common RAG use cases.
Customer Support Chatbots
RAG strengthens traditional chatbots by linking their responses to real-time documents like product manuals or policies. They can work with a wide range of queries that go much beyond standard FAQs.
Order Status Updates
To facilitate the delivery support for customers, businesses can introduce RAG. This technology will allow platforms to work with natural language queries like “Where is my package?”. Such tools retrieve order tracking data and shipping estimates to generate real-time, contextual answers.
For example, a food delivery platform DoorDash has already implemented a RAG-powered chatbot to increase the quality and efficiency of delivery support.
Personalized Post-Purchase Support
RAG can work with different sources, including order history and warranty documents. It means that RAG systems can answer questions about each user’s purchase.
Even if a customer asks very specific questions like “Is my smartphone still under warranty?”, an RAG-powered system will be able to provide an accurate reply. It will pull order data and warranty terms to share precise data.
If you want to know how retrieval augmented generation is used in other domains, we recommend you read about the EdTech project that our team worked on. We helped our client build an AI tutor powered by RAG to provide more informed feedback.
Pitfalls of Using RAG in E-Commerce
As we have discussed above, the use of RAG can bring a lot of benefits for e-commerce businesses and their customers. Nevertheless, there are also some risks and challenges related to the implementation of this technology. That’s why, if you consider its introduction, you should stay aware of the potential pitfalls and be ready to address them.
LLM for Ecommerce: Hallucination Issues
Today, RAG solutions heavily rely on large language models. One of the most alarming things about this is that LLMs can hallucinate and provide misleading or incorrect data.
Hallucinations of an LLM for ecommerce can result in recommendations of products that don’t exist or sharing wrong information about prices or warranty terms. All this may have a very negative impact on the reputation of your business.
To minimize hallucination issues, you can:
- utilize a closed-domain retrieval human-curated corpus;
- implement an additional lightweight system or rule-based tool to detect hallucinations;
- continuously analyze the situations when hallucinations happen to fine-tune systems.
Privacy Concerns
Personalization is directly related to processing user data, including needs, interests, behavior, purchase history, etc.
Improper use of data can lead to GDPR/CCPA non-compliance risks and leakage of sensitive information.
To mitigate such risks, it is recommended to:
- store and serve only anonymized or pseudonymized data;
- isolate sensitive user data from interaction with LLMs;
- introduce audit logs and strong access controls.
Infrastructure Costs
RAG requires a significantly more complex infrastructure than traditional LLM setups. It involves both a retrieval backend and a generation model. This leads to pretty high costs for deploying and operating such systems.
There are several ways to make the use of RAG retrieval augmented generation more cost-efficient.
For instance, to reduce repeated LLM calls, you can cache popular user queries and their corresponding results. Apart from this, it is possible to avoid relying solely on costly vector search. Instead, you can combine it with traditional keyword-based search.
Retrieval Augmented Generation in E-Commerce: Future Trends
RAG has already started to revolutionize customer experiences in e-commerce. Nevertheless, today, AI technologies are rapidly developing. Given this, it is obvious that everything that we can see now is just the beginning of a long journey. It’s time to consider how the application of RAG in e-commerce can change in the near future.
Mobile Apps Equipped with RAG
The traditional RAG architecture is not suitable for mobile devices. However, mobile commerce is a highly promising sphere today. Therefore, the need for RAG for mobile apps looks quite pressing. In this context, it is expected that RAG will be adapted for smartphones. There can be several approaches to this.
For example, RAG architecture can be split into edge and cloud components. Lightweight LLMs can work with the most common queries on-device. At the same time, more complex requests can be sent to cloud-based RAG systems.
Advanced Real-time Personalization
Traditional recommendation tools deal only with historical data. As a result, such personalization cannot be fully relevant, which decreases its efficiency. RAG can provide responses based on real-time behavior and the needs of users. RAG retrieval augmented generation helps to dynamically tailor product descriptions, offer additional items, and handle particular customer service requests in full accordance with the current preferences of each person.
AI Multimodality
The efficiency of a textual LLM for eCommerce has already been proven by many platforms. Now, the focus is on multimodality.
By integrating RAG with image recognition tools, voice assistants, and AR functionality, businesses can offer highly immersive user experiences. With such combined systems, users will be able to send an image and get instant replies in natural language. This function can be helpful for comparing prices, checking availability, and finding matching products.
Final Word
AI in general and RAG in particular can be viewed as a truly revolutionary technology for e-commerce. It makes customer experiences much more personalized and engaging, which has a positive impact on profitability. Today, a RAG system can become your strong competitive advantage that will make your business ready for the AI-powered future.
If you want to implement a system of this type tailored to the needs of your business and your customers, at Tensorway, we are ready to support you. With our solid expertise in genAI development and a deep understanding of the e-commerce domain, we will deliver a solution that will fully meet your requirements. Contact us to learn more about our experience and services.
FAQ
Why is RAG more powerful for e-commerce than traditional AI?
Traditional AI models rely solely on pre-trained data. At the same time, RAG combines real-time information retrieval with generative AI. Thanks to this, such tools can deliver more accurate, up-to-date, and context-aware responses. RAG can dynamically improve product recommendations, search relevance, and customer support interactions.
What technical infrastructure is necessary to implement RAG?
The introduction of RAG requires a vector database for fast semantic search, powerful embedding and language models for retrieval and generation, and a scalable cloud infrastructure. To manage data flow between the retrieval and generation components, it is also essential to implement an orchestration layer.
How can RAG combine personalization with the GDPR/CCPA requirements in data processing?
RAG works with anonymized or consent-based user data and retrieves only the necessary context for each query. As a result, such systems don’t store any personally identifiable information in the model itself. Data is processed dynamically and securely without violating privacy regulations.
What metrics, apart from cost reduction, help businesses measure RAG’s ROI?
It’s worth tracking such metrics as customer satisfaction scores (CSAT), conversion rates, and average order value. Also, it will be helpful to monitor engagement metrics, including query resolution rates and time-to-response improvements. They reflect how user experience and operational efficiency are enhanced.
