LLMs playing a pivotal role in transforming the search experience

A report from Elastic reveals that 81% of Indian organizations have adopted Generative AI, driving investments in AI initiatives. IT service providers are integrating GenAI for efficiency and productivity. Reportedly, IT service providers are increasingly integrating GenAI into their core development processes.

Sharing more insights on how AI is transforming search AI, Ravindra Ramnani, Senior Manager, Solutions Architecture, Elastic explains how the company is engaged in ensuring data security, compliance, and trust in AI-driven search solutions -

How is AI reshaping the way search engines understand and interpret what the user is searching for?

Artificial intelligence has transformed how search engines interpret user queries. In the past, the more common approach to retrieving information was lexical search.

Lexical or keyword search matches exact words or phrases in the documents to those in the search query. This approach generally works well on broad types of data but does not take into account the context or meaning of words. This can lead to irrelevant results when queries contain misspellings or a word that has multiple meanings.

Semantic search is a newer approach that has become popular in the last few years. It uses a branch of AI, natural language processing (NLP), to analyse and store a representation of the meaning of the document or paragraph, rather than storing the individual words. This approach produces more accurate and relevant results as it stores the meaning of words and phrases, which also allows it to better handle misspellings and synonyms.

Another impactful advancement in this space is the integration of generative AI, particularly through a Retrieval-Augmented Generation (RAG) framework. This allows search systems to combine large language models (LLMs) with real-time, relevant information drawn from proprietary knowledge sources. In fact, research from Elastic revealed that nearly 44% of global IT leaders believe generative AI in search could save employees up to two work-days a week, transforming search into a powerful driver of productivity.

In what ways are AI algorithms improving the accuracy and relevance of search results? How are they different from traditional search engines?

Before the advent of AI, search was predominantly run on the same approach - matching keywords. As a result, traditional search engines have limited understanding of query meaning.

On the other hand, GenAI search engines can understand the intent and context behind user queries. A user might search for “iPhone battery” and get results containing the exact words when using traditional engines. With AI, they might get results on replacement batteries, extending battery life or troubleshooting battery issues depending on factors like past searches and past behaviour. 

Additionally, searches that involve different modalities like images, video and audio would require different search systems. AI-driven search is able to understand multimodal queries and generate results that are more relevant. Using AI, a search for “how to fix a bicycle flat tire” can return relevant video tutorials on top of text instructions. 

GenAI searches are able to present information from multiple sources, unlike traditional searches that present disparate results from different sources. Users may find using GenAI search engines more intuitive as their searches take the form of a conversation, where the engine maintains context and can continue generating responses based on earlier queries. Traditional search engines are unable to do so, with each query treated as a distinct, separate query.

What role do Large Language Models (LLMs) play in enhancing AI-driven search capabilities and what are their challenges?

Large Language Models (LLMs) are playing a pivotal role in transforming the search experience by introducing intelligent, context-aware responses that go far beyond traditional keyword matching. Rather than simply retrieving documents based on static terms, LLMs are capable of understanding user intent and grasping the meaning behind queries to enable      more relevant, conversational, and nuanced outputs.     

While the introduction of LLMs can greatly enhance search capabilities, organisations must be aware of the potential challenges to be better prepared.

AI-driven search results are highly dependent on the quality of data that LLMs are trained on. Should the training data be full of errors and biases, it can affect the model’s output performance and negatively impact downstream training, which refers to the training or fine-tuning of an existing LLM for custom tasks.

This can lead to anchoring bias, where an LLM relies heavily on the initial piece of information it receives to make subsequent judgments, even if that information is irrelevant or inaccurate. This means that even if a GenAI search engine is capable of retrieving information from multiple up-to-date sources, anchoring bias may give rise to flawed results or hallucinations.

To avoid such scenarios and to boost the efficacy of LLMs, companies should look at deploying a search platform that allows timely access to the precise data required and is presented in context.

Are the ethical concerns when implementing AI in search taken into consideration, particularly regarding bias and fairness in search results? How does Elastic ensure accuracy, relevance, and bias mitigation in AI-powered search results?

While many organisations are keen to reap the benefits of AI-driven search, being aware of ethical concerns will help to mitigate or avoid bias and fairness issues. 

LLMs can inherit biases inherent in their training data that may lead to misleading or inaccurate search results. This poses a significant concern for search applications where factual accuracy is critical. In areas like recruitment or financial lending, such biases may led to unfair outcomes and raise ethical considerations. For example, certain demographics may be excluded when it comes to the successful application of personal loans.

AI can be a massive driver of business transformation, but organisations must adopt measures to tackle bias and fairness.

The Elasticsearch Relevance Engine (ESRE) plays a key role in ensuring accuracy, relevance, and bias mitigation in AI-powered search results by combining semantic search with hybrid ranking methodologies. This intelligent integration allows search results to be both accurate and adaptable while minimizing inherent biases often found in LLMs. Elasticsearch       delivers RAG by grounding generative AI outputs in real-time, contextually relevant data from Elastic’s indexes, thus avoiding the hallucinations and misinformation common in unanchored LLM outputs.

To ensure fairness and transparency, Elastic aligns its AI practices with trusted frameworks like the NIST AI Risk Management Framework, focusing on governance, measurement, and responsible deployment. Furthermore, Elastic’s AI Assistant for security adds another layer of protection, with built-in safeguards against prompt injection, data leakage, and unauthorized access, supported by custom rule sets and real-time alerts. 

Elastic also supports ethical AI through data quality controls. The Data Quality Dashboard helps users assess, correct, and maintain clean datasets, giving confidence in the reliability of AI outputs. This ensures that results are not only accurate but also trustworthy and traceable.

What measures does Elastic take to ensure data security, compliance, and trust in AI-driven search solutions?

Elastic is committed to providing products and services that have been tested in a real production environment before they’re distributed broadly - we are an enthusiastic customer zero for all of our solutions. This approach also includes security, where we use our own Elastic Security solution across the organisation. The solution helps teams protect, investigate, and respond to threats before damage is done.

Elastic Security also functions as a security information and event management (SIEM) and extended detection and response (XDR) solution, offering endpoint and cloud protection that adapts to modern threat landscapes while integrating easily with existing IT ecosystems. Another feature of Elastic Security is role-based access control (RBAC). It enables organisations to control access to resources by assigning privileges to roles and assigning roles to users or groups.

Elastic protects our customers’ cluster data against unauthorized access, modification, or deletion through our formal information security management system (ISMS) that’s certified on ISO 27001 — including ISO 27017 and ISO 27018. Our Information Security Governance Policy serves as the backbone for all information security policies, standards, and guidelines.

As Elastic Security is built on the Elastic Search AI Platform, we are committed to ensuring that the platform complies with global data protection laws, including India’s Digital Personal Data Protection (DPDP) Act and the General Data Protection Regulation (GDPR).   Elastic also aligns with both global and regional regulatory standards, including SOC 2, ISO 27001, and country-specific frameworks such as the Reserve Bank of India’s guidelines and SEBI’s Cybersecurity and Cyber Resilience Framework (CSCRF).

All of these work in tandem to ensure data security, compliance, and trust in AI-driven search solutions.

What innovations in AI are likely to drive the next generation of search capabilities over the next few years?

The deluge of data across the Internet and within organizational databases often presents hurdles for companies to quickly find what they need. Leveraging AI-based capabilities have allowed modern search systems to drastically improve search results.

For instance, facet search in Elasticsearch can help users to quickly narrow down options for improved search scope and results. We can use AI to create new categorizations that go beyond the traditional classifications in the index.

One example of how this can be used is with movie classifications. It’s common for most streaming services to categorize movies according to the standard genres like action, drama or science fiction etc. By leveraging AI to analyse movie synopses and capture the subtle differences between films, new categories can be created to enable a more meaningful refinement of results.

Movies like “American Beauty” and “Lost in Translation” would usually be found under the Drama category, but with facet search, a new category like Mid-life Crisis could      be created to offer more useful filters to users, resulting in more precise searches, enabling users to find exactly what they’re looking for with greater ease.

Advancements in both AI and search will lead to more powerful and exceptional search experiences. After all, effective AI-driven search is highly dependent on having access to accurate, relevant and timely data. Search enhances the relevance and quality of data that AI systems consume, improving model outcomes and enabling faster, more reliable results and insights.