Where Do Large Language Models Fit Into the Future of Ecommerce Search?

Paul Edwards
Paul Edwards

All of us have used a search box. Its purpose is well defined — you type something in, and you get results to what you were looking for. If you’re on an ecommerce site, those results are products or services. Well, that’s what should happen, anyway, but not every search vendor is capable of serving up results that are relevant, personalized, and optimized for important ecommerce metrics like conversions, revenue, and add-to-cart rate.

Despite the holes already in the market, an evolution is happening underneath the current landscape of ecommerce site search, and it’s made a big splash in the court of public opinion over the past year. That’s right — generative AI has entered the chat with ChatGPT, one of many foundation models now available. More people are also curious about the tech behind generative AI, known as “large language models” or LLMs, which use massive amounts of data to learn, respond, and even generate human-like language for different queries. The power of LLMs has even been compared to the human brain. 

However, we should understand that LLMs are a two-sided coin — on one side, there are all the possibilities for deep learning and innovation, and on the other, there is a fair amount of risk. While large language models show a lot of potential, they also have a long way to go in terms of development (currently, valid questions are being raised about the ethics and potential risks associated with the global AI race). It’s especially important to remember that LLMs hold a lot of powerful (and dangerous) capabilities, like “Do Anything Now,” where people can subvert the AI’s purpose for their own amusement or gain.

All in all, it isn’t a perfect technology — although pre-trained, LLMs require ongoing refinements and adjustments to ensure consistent accuracy and relevance. Even then, a big question on the minds of commerce professionals remains: Where do LLMs fit into a shopping scenario? Is it possible now?

Chatbot with LLM Communicating with a Customer

Since artificial intelligence has never been (and will never be) a finite line drawn in the sand, this question is more complex and multi-faceted than it seems.

AI Should Already Be Embedded in Ecommerce Site Search 

AI in ecommerce search today drives revenue by using a combination of recall and ranking algorithms and training data to deliver the most relevant results that match your shopper’s intent. This happens through a combination of a transformer model, like natural language processing (NLP), and machine learning (ML). NLP breaks down the context behind shoppers’ queries, and ML learns about your business from analyzing these behaviors. This self-learning AI becomes smarter daily and can rapidly adapt to your customer’s ever-changing needs as you continue to scale.

But, your business will need to see your customers as more than just “customers” to succeed in the current competitive landscape. The winners in ecommerce find the “seekers.” They look beyond a person’s singular identity as “the consumer” to uncover their true motivation behind wanting a certain product or service, then build digital shopping experiences specific to them. This is where AI and machine learning models can help tremendously, whether it’s crunching product and customer data to deliver insights and optimization suggestions or offering recommendations based on similar products. 

As we look toward the future, we must recognize that the potential for LLMs to revolutionize ecommerce search lies in their ability to provide shoppers with the information and results they need without the typical prompting required of a traditional search bar.

Why Should Ecommerce Care About LLMs and Generative AI?

As we all probably know, ChatGPT took the conversation about generative AI to the mainstream and provided an environment where anyone could interact with it. Not only has it caused a lot of discussion around the current uses of AI within the ecommerce industry, but it also has a lot of professionals wondering what LLMs look like when we picture the future of ecommerce.

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LLMs have a lot of potential use cases within the ecommerce industry, especially when it comes to brands and retailers assisting their customers through the search bar: 

  • LLM-Based Precision – Leveraging a large language model for search optimization marks a significant evolution in product search by making torso and long-tail searches, like “men’s running shoes” or “women’s hiking boots size 8,” more precise and relevant. LLM-based precision broadens search coverage across both broad and highly specific queries — crucial for linking users with products they’re truly interested in — which highlights the immense value of understanding search intent.
  • LLM-Based Search Recall – Integrating tools like synonyms, spell corrections, and relaxation rules with LLMs enhances search recall by identifying similarities and semantic meanings in queries, surpassing traditional text-match systems. This approach improves search results for nuanced needs, such as “running shoes good for knees,” by focusing on semantic intent rather than just keywords.
  • Virtual Shopping Assistants – Powered by LLMs, vector technology, and personalization, virtual shopping assistants are set to transform online customer service into a conversational, intuitive experience that goes beyond chatbots. By understanding complex queries, connecting shoppers with suitable products through semantic recognition, and tailoring interactions based on customer data, conversational commerce merges the convenience of digital shopping with the personalized touch of in-store interactions.

It all comes back to the seeker and unlocking the full potential of their customer journey using generative AI. Take, for instance, when somebody is looking to decorate their living room with neutral, earthy tones, but they don’t know how to verbalize this “want” into a search query. Although the search terms might be unknown to the seeker, an LLM can bring context to their particular need through a simple conversation. 

We just have to think of AI as the digital version of the Industrial Revolution — language models will revolutionize our world and how we live. Nonetheless, we must balance the innovation and creativity inherent in language models with human ingenuity.

Next Phase of Technological Innovation - Generative AI

Since the ecommerce industry is laser-focused on building better shopping experiences, we shouldn’t be afraid to remain open-minded to future possibilities. If you’re interested in learning about large language models or artificial intelligence in general, check out the Bloomreach blog


Paul Edwards

Technical Product Strategist at Bloomreach

Paul is a Technical Product Strategist versed in fields as diverse as ecommerce, machine learning, telecommunications, and aviation. He spends his time focussing on the application of cutting-edge technologies and hybrid algorithms in the ecommerce space. His spare time is consumed with the renovation of a cottage in the Surrey hills.

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