What is automatic search relevance tuning in SearchAI?

Automatic search relevance tuning in SearchAI uses LLM‑powered reranking to continuously optimize search results. It eliminates manual tuning by reranking initial keyword or vector matches to surface the most relevant content first.

How does hybrid search and LLM reranking work together in SearchAI?

SearchAI combines hybrid search—blending traditional BM25 keyword search with semantic vector search—with an LLM‑based reranker. The initial search results are reranked contextually so that intent‑based relevance surfaces preferred results automatically.

What are the benefits of using LLM‑powered contextual reranking?

LLM‑powered reranking improves recall and precision by reassessing the top-ranking candidates using deeper context. It surfaces relevant documents that may not have ranked high in initial search, enhancing user satisfaction and relevance.

What metadata enhancements are included in SearchAI’s relevance tuning?

SearchAI includes intelligent metadata enhancement alongside LLM reranking. Metadata—such as titles, summaries, and topic tags—is auto-generated to boost discoverability across structured and unstructured content.

Why is hybrid search with LLM reranking important for enterprise search?

In enterprise environments, hybrid search with LLM reranking addresses the inadequacies of keyword-only systems. By combining context-aware semantic matching and contextual scoring, SearchAI delivers precise, intent‑driven results across complex data repositories.

Is SearchAI’s relevance tuning solution scalable for large datasets?

Yes. SearchAI scales to large enterprise deployments by leveraging hybrid search and LLM reranking on initial candidate sets. This architecture ensures fast, accurate results without compromising performance or requiring manual relevance calibration.

NEW! Reimagining the eCommerce Search Experience with SearchAI Get Started.

NEW! Reimagining the eCommerce Search Experience with SearchAI Get Started.

NEW! Reimagining the eCommerce Search Experience with SearchAI Get Started.

Home

SearchAI

Enterprise Search

Solutions

Pricing

Resources

About

understanding the four stages of enterprise search white paper video background image

ARTIFICIAL INTELLIGENCE

Generative AI

SearchAI

User Experience

Automatic Search Relevance Tuning with Hybrid Search and LLM-Powered Contextual Reranking

Q: Where can I learn more about SearchAI’s automatic relevance tuning?

You can explore the official SearchBlox blog entry titled ‘Automatic Search Relevance Tuning with Hybrid Search and LLM‑Powered Contextual Reranking’ to understand the architecture, benefits, and real-world use cases of SearchAI’s relevance tuning.

February 07, 2025

Search administrators and content managers have long grappled with relevancy tuning - the painstaking process of analyzing user behavior and manually adjusting search parameters to surface the most relevant content. The SearchAI Platform transforms this traditionally labor-intensive task by leveraging Large Language Models (LLMs) to optimize search relevance automatically. Here's how it works:

Enthusiasm for productivity and efficiency gains across an extensive range of use cases, including generating content, discovering insights, automating processes and facilitating innovation.

Risk of feeding Large Language Models (LLM) with highly sensitive proprietary data, vulnerable to data leakage. And caution with GenAI dependencies and spiraling usage amongst employees, leaving an unexpected surge in AI costs when managing multiple GenAI applications spread across multiple users.

The question that enterprises need to answer is.

Intelligent Metadata Enhancement
Poor document metadata can severely impact search effectiveness, especially when dealing with large collections of PDFs and Office documents. During indexing, the SearchAI Platform automatically generates clear, concise titles, descriptions, and topic tags. This enhanced metadata improves document findability and provides users with a meaningful preview in search results, enabling faster identification of relevant documents.

Fixed Costs: Feel Liberated from Unexpected GenAI Expenses

The financial implications of implementing Generative AI can be unpredictable. As organizations scale operations using GenAI, hidden expenses related to embedding, inference costs, and token usage fees (key processing that drives AI model operations) can escalate rapidly with growing usage, impacting overall ROI. There’s also the build vs. buy conundrum, with enterprises caught in a vicious cycle of overspending on customized solutions.

SearchAI offers a controlled approach to AI implementation, allowing businesses to better oversee their AI expenditures while auto-scaling operations and performance—at any level. SearchAI’s out-of-the-box GenAI solutions are available through a fixed-cost licensing model, providing budget predictability and eliminating surprises associated with variable pricing structures.

Fixed Costs: Feel Liberated from Unexpected GenAI Expenses

PageDNA: Context-Aware Document Understanding
Matching user intent with document content becomes increasingly complex as documents grow in size. The SearchAI Platform addresses this challenge with PageDNA - a revolutionary metadata field generated by LLMs. This unique document fingerprint captures the essential context of each document, ensuring more accurate matching between user queries and relevant content.

Protect Your Data: Make Data Impenetrable with Private LLM’s
Data security is paramount to reinforcing trust and value in Enterprise GenAI. Many AI systems necessitate sending sensitive data to external platforms, raising concerns about its potential use for training other models, the risk of confidential information leaks, and the overall security of proprietary information.

SearchAI prioritizes data protection using Private LLMs, ensuring that sensitive data remains within the confines of your organization's control. This ensures fortified security of data with the flexibility to deploy on-premise or on the cloud - all while ensuring that data is protected in-transit, at-rest, or in-use.

Simplify Work: Built to Accelerate Work, not Replace Employees

There is a notion that AI will lead to widespread job displacement, removing things (including humans), slowing down key processes and the speed to market. Contrary to popular perception, GenAI will usher in more cohesive collaboration by augmenting human expertise with AI capabilities to accelerate your work.

SearchAI puts human-AI collaboration to the forefront, where every interaction, insight, and innovation comes with human-in-the-loop controls. With multiple GenAI offerings on a single platform (RAG, Hybrid Search, AI Chatbot, and AI Agents), SearchAI automates many routine low-value tasks, freeing up time for human contribution to high-value work. Designed to enhance employee capabilities and optimize existing workflows, SearchAI transforms employees into "super-efficient" versions of themselves.

Hybrid Search Architecture

The platform combines the precision of keyword search with the flexibility of vector search to deliver comprehensive results:

- Keyword search excels at exact matches, perfect for finding specific names or product IDs

- Vector search understands conceptual similarities, handles typos gracefully, and processes natural language queries effectively

This hybrid search approach ensures users find relevant documents even when using imperfect or conversational search terms.

Hybrid Search Architecture

The platform combines the precision of keyword search with the flexibility of vector search to deliver comprehensive results:

- Keyword search excels at exact matches, perfect for finding specific names or product IDs

- Vector search understands conceptual similarities, handles typos gracefully, and processes natural language queries effectively

This hybrid search approach ensures users find relevant documents even when using imperfect or conversational search terms.

Advanced LLM-Powered Results Reranking
While hybrid search provides a solid foundation, the SearchAI Platform takes an additional step to optimize relevance. The platform employs LLM-based reranking as a final refinement stage, individually scoring each result against the user's query. This sophisticated process ensures search results are ordered by contextual relevance to the specific user query and the documents.

Single Platform: To Simplify all your GenAI Deployments
Typically, enterprises invest in multiple GenAI systems, most of which are standalone systems requiring separate data ingestion pipelines. This results in a siloed environment forcing makeshift integrations that create complexity, reduce efficiency, and increase costs - all while failing to deliver optimal business value.

SearchAI unifies Enterprise Search, RAG, Chatbots, and AI Agents through its Integrated RAG and Hybrid Search Platform. SearchAI eliminates the complexity of managing multiple data feeds and integration points. Organizations can deploy and control all AI capabilities through a single interface, significantly reducing operational overhead while maintaining data accuracy across applications.

The Future of Search is Hybrid

The SearchAI Platform dramatically improves the search experience by eliminating manual tuning with intelligent, query-specific optimization. Users can now focus on finding the most relevant information rather than wrestling with irrelevant results. This AI approach to relevance represents a significant leap forward in enterprise search technology, making information discovery faster and more relevant.

Reserve your private, custom demo.

See how SearchAI Hybrid Search supports both customer and employee use cases.

Schedule Your Private Demo

E-mail

Facebook

Twitter

E-mail

Facebook

Twitter

E-mail

Facebook

Twitter

Are the risks associated with Generative AI (GenAI) worth the rewards?

See What's Possible

SearchAI emerges as a solution for businesses seeking to integrate the capabilities of Generative AI while mitigating potential risks. This blog delves into how SearchAI addresses key concerns surrounding cost management, data security, and workforce impact, with Single, unified RAG and Hybrid Search platform - offering a balanced approach to AI adoption.

A Whole New Conversation