Using Hybrid Search to Improve In-App Product Search

Introduction

When it comes to application experiences, it’s all about exceeding your user’s expectations while minimizing friction and frustration. A common way to blow a user experience is when end users spend too much time finding the information and features they need. Once you have lost them, it’s almost impossible to get them back. These types of poor experiences will almost always result in low product adoption and poor user sentiment. Digital products that deliver a comprehensive navigation and search experience mean that product developers design their applications with richer content, features, and services without the risk of confusing the user.

What is Hybrid Search?

For many years, in-app product search was dominated by keyword matching. While keyword search (BM25) has its strengths, it needs to improve in some key areas around understanding of intent and synonyms. Keyword search systems also often have difficulty understanding searches and queries across languages. In the past two years, we have seen a big push from search systems into question/answering systems. These are systems that understand the context, language, and intent of questions more thoroughly and are leveraging Large Language Models (LLMs). These new semantic search capabilities are paving the way for the real features that product builders are itching to get their hands on: Generative AI.

With Generative AI applications, often the outcome is only as effective as the initial search and retrieval. To ensure the most optimal generative results, employing multiple search approaches to yield the most comprehensive results for the development of chatbots and question/answer systems. This is called Hybrid Search.

Hybrid Search uses large language model (LLM) based and Keyword (Boolean exact) retrieval to retrieve the most relevant products, support cases, and documents that answer your users’ questions first. Hybrid search supports models that are optimized for semantic understanding but also for exact and boolean for things like product and person names. Many other models are good at one, but not both at the same time. discuss the evolution of keyword, semantic, and LLM-retrieval-based systems and the road to widespread adoption.

Hybrid Search Delivers Improved User Experience

By leveraging this mix of techniques, product and application builders can experience the combined benefits of leveraging the strengths of both approaches, ensuring that the end answer has the benefit of better accuracy. Here are some additional benefits that users and product builders should notice when leveraging a hybrid search approach.

Better Search Accuracy: By leveraging a mix of semantic and keyword searches, the system understands user intent and context, which leads to more accurate search results.
Intuitive User Interaction: Hybrid Search allows for natural language queries, which allows the user to speak in common language and retrieve relevant search results. This boosts user-friendliness and ensures a consistent experience across all types of queries.
Personalized Engagement: Hybrid Search delivers personalized search results based on user behavior and preferences.

Superior Cross-language Support: Hybrid Search systems like Vectara support search queries across different languages and well as deliver retrieval results across languages. This is critical for improving the experience for non-English-speaking users

Hybrid Search Improves Your Bottom Line

Implementing Hybrid Search is not only going to equip you with a cool new technology pattern but also promises to affect your bottom dollar. By implementing a hybrid search system to increase user loyalty and reduce onboarding friction and costs, companies can also expect to see increased user satisfaction, better customer loyalty scores and NPS, and reduced churn rates. By delivering a better in-product experience, organizations should also see better conversion rates as better relevant search should lead to better product and service discovery. A practical product feature leveraging hybrid search should also help create new revenue streams for the business through better conversion and competitive differentiation. In these scenarios, it really is all about the cost of not implementing a better user experience. In competitive markets, this miscalculation can often be far worse than the impact of innovating.

Vectara’s Hybrid Search in Action

Hybrid Search Accuracy Adjusted by Lambda

Vectara provides an end-to-end platform for development across the entire lifecycle of Generative AI. Critical to delivering extraordinary results is a comprehensive approach to search to gain the most insight into the intent, context, and relevance of the query. This is why Vectara users get all the benefits of hybrid search through a simple-to-use API. The Vectara platform utilizes best-fit LLMs across the platform, assigning the best fit for search, retrieval, and summarization. We do this all so you don’t have to, and we provide you with the best approach for your experience. When searching, users can dial in the amount of keyword or LLM-based search to fine-tune their queries across use cases. Vectara also provides guidance on the best mix settings for hybrid searches as well as explanations of relevance scores. Some users even filter out hits that fall below a certain threshold, to ensure better accuracy of answers. The platform goes one step further and grounds its responses in the facts (your data) to ensure that the most relevant context is most influential. We call this grounded generation. In short, Vectara gives you all the power of hybrid search while highly customizable through an easily embedded platform that users of all levels of expertise can leverage. Vectara is free to get started; try out our hybrid search for yourself!