How Neural Hybrid Search Modernizes and Evolves Your Search Maturity

These days, customers (and workplace users) expect more from search. They want instant access to the right information, at the right time, in the format they prefer. Businesses that can’t deliver this risk falling behind.

The challenge? Search technology changes fast. What was once cutting-edge is now just the basics. Traditional keyword matching, for example, used to be enough. But today, it’s barely scratching the surface. The rise of Generative AI (Gen AI) promises to redefine what’s possible — but it also brings new challenges. So, how do you stay ahead? Could the answer lie in Neural Hybrid Search?

Neural Hybrid Search combines the precision of keyword-based searches with the contextual understanding of Semantic Vector search. In taking this search approach, Lucidworks makes Semantic Vector search easy to deploy while enhancing the intelligence of lexical techniques. This balance of Lexical precision and Semantic understanding allows businesses to offer a more accurate and personalized search experience — critical at every stage of search maturity.

Why Combine Lexical and Semantic Search?

Lexical Search matches exact keywords, ensuring precision. Semantic Vector Search understands context, uncovering the intent behind those keywords, to ultimately retrieve relevant results even if the exact words aren’t used. This combination (hybrid search) balances accuracy with contextual understanding.

Let’s explore how Neural Hybrid Search can take your search capabilities to the next level.

Stage 1: Moving Beyond Basic Keyword Matching

At the beginning of your search journey, accuracy is key. Traditional search methods rely on simple keyword matching, but they can fall short when users don’t use the exact phrasing in their queries.

Neural Hybrid Search helps overcome these limitations by understanding the intent behind the words. It blends the precision of keyword search with AI-driven semantic understanding. This ensures your users get relevant results, even if they don’t use the exact terms found in your content.

Example:
If a customer searches for “printer troubleshooting,” a basic keyword search might only return results that match those exact words. Neural Hybrid Search, however, recognizes that the customer is looking for printer help and can also suggest related content like “fixing printer issues” or “common printer errors.”

How Neural Hybrid Search Works (In a Nutshell) 

• Lexical Search: Provides structure and precision by delivering exact keyword matches.
• Semantic Search: Understands synonyms and the context of queries, uncovering the user’s true intent.

Stage 2: Personalization and Context Matter

As your business grows, so do your customers’ expectations. They want search results that are tailored to their needs. To do this, you need to consider factors like user behavior, location, and even access permissions.

Neural Hybrid Search adapts in real-time, delivering personalized results while respecting data access controls. It ensures each user sees what’s most relevant to them. When combined with flexible security trimmings, businesses fortify data accessibility and privacy capabilities by making data and resources accessible based on a user’s entitlements.

Example:
In an e-commerce setting, Neural Hybrid Search can use a customer’s browsing history to recommend products that align with their preferences. If you’re serving B2B clients, it can filter results based on industry or show only contracted items, making the experience both relevant and efficient.

“B2B commerce organizations struggle with catalog discrepancies, language nuances, and stock discrepancies—all of which negatively impact the buying experience. Neural Hybrid Search is so promising because it automates solutions for these big issues, helping teams provide relevance and improve outcomes with less work.” — Eric Immermann, Practice Director, Search and Content, Perficient

Search Personalization in Action

With Neural Hybrid Search, you can:

• Adapt results based on user roles and entitlements.
• Combine Semantic and Lexical searches to deliver personalized, secure experiences.

Stage 3: Grounding Generative AI in Reality

Generative AI, especially models like Retrieval-Augmented Generation (RAG), has opened up new possibilities for search. However, they come with a significant challenge: hallucinations. AI might generate responses that are misleading or simply incorrect.

This is where Neural Hybrid Search shines. It acts as a reliable foundation for Gen AI initiatives, providing AI models with accurate and contextually relevant data, reducing the chances of hallucinations. Your AI can now generate results that are both reliable and insightful.

Example:
Imagine using Gen AI to answer a researcher’s technical questions. Without proper grounding, the AI could miss key points or introduce errors. But with Neural Hybrid Search, it has access to a rich source of verified information, ensuring the summary is accurate and concise.

How Neural Hybrid Search Grounds Generative AI

• Lexical Search ensures fact-based, structured data retrieval.
• Semantic Search helps AI understand context and intent, reducing the risk of “hallucinations.”

Final Thoughts: Elevate Your Search with Neural Hybrid Search

Neural Hybrid Search isn’t just a search tool — it’s a game-changer. Whether you’re just starting with search or looking to harness the power of Generative AI, it gives you the foundation to offer more accurate, relevant, and personalized search experiences.

Ready to take your search to the next level? Explore how Neural Hybrid Search can transform your business and help you meet your users’ evolving needs.