The Modernization and Evolution of Your Search Maturity via Neural Hybrid Search

Introduction

Customers (and office users) have higher expectations for search these days. They desire immediate access to the appropriate information in their preferred format at the appropriate moment. Companies who are unable to meet this risk falling behind.

The difficulty? Search technology is constantly evolving. What used to be innovative has become rudimentary. For instance, traditional keyword matching was sufficient in the past. Today, however, it hardly touches the surface. Although the emergence of generative artificial intelligence (Gen AI) holds the potential to redefine what is feasible, it also presents new difficulties. So, how do you maintain your lead? Is Neural Hybrid Search the solution?

Why Merge Semantic and Lexical Search?

The contextual awareness of Semantic Vector search and the accuracy of keyword-based searches are combined in Neural Hybrid Search. By using this search strategy, Lucidworks improves the intelligence of lexical approaches while making Semantic Vector search simple to implement. Businesses are able to provide a more precise and customized search experience because of this harmony between lexical accuracy and semantic comprehension, which is essential at every level of search maturity.

Step 1: Going Beyond Simple Keyword Correspondence

Accuracy is crucial when you first start your search. Conventional search techniques depend on basic keyword matching, however they may not be effective if users' searches don't contain the correct wording.

By comprehending the meaning behind the words, Neural Hybrid Search helps get over these restrictions. It combines AI-driven semantic comprehension with the accuracy of keyword search. This guarantees that even if readers don't utilize the precise phrases in your content, they will still see relevant results.

For instance, a simple keyword search may only yield results that contain the precise words "printer troubleshooting" if a customer searches for it. However, Neural Hybrid Search can also provide relevant content, such as "fixing printer issues" or "common printer errors," as it understands that the user is looking for printer assistance.

A Brief Overview of Neural Hybrid Search Technology

Lexical Search: By providing precise keyword matches, it offers organization and accuracy.

Semantic Search: Defines the user's actual purpose by comprehending synonyms and query context.

Step 2: Context and Personalization Are Important

Your clients' expectations will rise along with your business. They want results from searches that are specific to their requirements. You must achieve this by taking into account variables like location, user behavior, and even access rights.

Real-time adaptation allows Neural Hybrid Search to provide tailored results while adhering to data access regulations. It guarantees that each user sees the content that is most pertinent to them. Businesses strengthen data accessibility and privacy capabilities by making data and resources available according to a user's entitlements when paired with flexible security measures.

For instance, in an online store, Neural Hybrid Search can make product recommendations based on a customer's browsing history. It can show only contracted products or filter results by industry if you're offering business-to-business clients, making the experience efficient and pertinent.

Language nuances, stock discrepancies, and catalog mismatches are problems that B2B commerce firms face and have a detrimental effect on the purchasing experience. Because it automates answers for these major problems, neural hybrid search holds great promise for assisting teams in providing relevance and improving results with less effort. — Eric Immermann, Practice Director, Search and Content, Perficient

Personalization of Search in Practice

Neural Hybrid Search allows you to:

• Modify results based on user roles and privileges.
• Deliver individualized, safe experiences by combining lexical and semantic searches.

Step 3: Bringing Generative AI Back to Earth

New search options have been made possible by generative AI, particularly models such as Retrieval-Augmented Generation (RAG). They do have one major drawback, though: hallucinations. AI may produce answers that are inaccurate or deceptive.

Neural Hybrid Search excels in this situation. It serves as a solid basis for Gen AI projects by giving AI models precise and contextually relevant data, which lowers the likelihood of hallucinations. Your AI is now capable of producing accurate and perceptive findings.

As an illustration, consider applying Gen AI to respond to technical inquiries from researchers. The AI could make mistakes or overlook important details if it is not properly grounded. However, Neural Hybrid Search has access to a wealth of validated data, guaranteeing that the synopsis is precise and succinct.

How Generative AI Is Grounded in Neural Hybrid Search

Lexical Search: Guarantees structured, fact-based data retrieval.

Semantic Search: By assisting AI in comprehending context and intent, it lowers the possibility of "hallucinations."

Conclusion: Use Neural Hybrid Search to Improve Your Search

Neural Hybrid Search is revolutionary; it's more than just a search engine. It provides the framework for providing more precise, pertinent, and customized search experiences, regardless of your level of experience with search or your desire to fully utilize generative artificial intelligence.