How to Make the Most of AI-Powered Search
Over the past decade, e-commerce search methods have evolved from simple text-based searches to more advanced, semantic, vector-based searches. Generative artificial intelligence (GenAI) and large language models (LLMs) drive this transformation, elevating search to a new level by providing context-aware, personalized results. Customers now expect precise results using conversational language and advanced image and voice search. The ability to provide highly relevant AI predictions and tailored results to users is possible for more retailers and businesses than ever before.
The Power of GenAI and LLMs
Traditional search engines rely on matching customer queries to product descriptions based on keywords. This method is a far cry from natural human language, and it’s often difficult to capture exactly what a customer wants. For example, the terms “couch” and “sofa” may turn up different results. With AI-powered search engines and technologies like semantic search and vector matching, retailers have broken free of rigid, keyword-based search requirements, enabling customers to use more natural language in search.
Using natural language processing (NLP), semantic search allows search engines to understand the context and intent behind a query. Vector matching takes the semantic query and matches it with other products that aren’t identical but are semantically similar. This technology helps identify the similarities and differences between terms like “slacks,” “khakis,” and “jeans.” The shift toward natural human conversation in search lets users interact with e-commerce platforms as they would with a knowledgeable sales associate. For example, CarGurus recently added an AI-powered search where customers can ask questions like, “What cars are good for tall drivers?”
Retailers can also achieve improved customer contextualization and personalization with GenAI. Google’s AI considers factors like “the relationships between words, the searcher’s location, any previous searches, and the context of the search” when delivering its search results. By understanding a customer’s history and context, a retailer can better anticipate user intent and offer improved recommendations.
Effective Use and Design
When leveraging GenAI and LLM, careful design and deployment choices address cost, performance, and security concerns while achieving organizational goals. Building an LLM in-house eliminates the costs of using a third party, saves money in the long run, and allows the model to be tailored to the organization’s specific product catalogs and customer bases. A smaller, more efficient model is created from a larger LLM using LLM distillation while maintaining performance and reducing costs.
Deploying GenAI in the right place reduces latency and improves performance. GenAI also caches frequent queries and conducts offline processing to optimize performance and reduce expenses. Maintaining security and privacy is critical when leveraging AI models. By building robust guardrails into the model, businesses ensure results are properly validated and appropriate, mitigating potential legal and reputational risks.
The Future of Improved Interfaces
Customer expectations for search are changing, especially for younger generations who increasingly seek more advanced technologies like image search to find what they’re looking for. Virtual try-on has been common in the direct-to-consumer eyewear market for years, and companies like Wayfair and Ikea now offer methods to visualize furniture in customers’ homes virtually. As GenAI technology becomes more powerful, customers increasingly use and expect retailers to offer functional multimodal search, including image and voice.
Search technology advances reshape how customers approach e-commerce. Semantic search marks a considerable evolution from keyword matching, providing meaningful results without requiring specificity on the consumer’s part. Image search lets customers identify specific items with minimal effort. As these technologies become more powerful, customer expectations grow. Leveraging AI and LLMs will benefit retailers who effectively balance technological foresight and business needs and give them a competitive advantage for success.
Vivek Agrawal is a senior director of software engineering for a leading e-commerce retailer.

Vivek Agrawal is a senior director of software engineering for a leading e-commerce retailer. He has over 17 years of experience working with large-scale systems, information retrieval, runtime scaling, and performance. He currently manages a team of over 80 engineers responsible for a search runtime platform serving more than 200 million requests per day. Vivek holds an M.tech in electrical engineering from IIT Kanpur in Kanpur, India. Connect with Vivek on LinkedIn.